Week 1: PRINCIPLES & PRACTICES

Week # 1 Homework

Principles & Practices

A look at the ethics, safety and security considerations for a biological engineering application with the proposed governance policy goals and actions.

Most countries like Kenya in the developing countries have a waste problem that causes a lot of health issues to the people who live near them while damaging the ecosytem around them that creates a burden for the country in dealing with the financial implications. Synthetic genomic has made it possible through the use of biological organisms that clean up environmental waste and simultaneously produce energy, making this one of the most active fields in biotechnology often referred to as the Circular Bioeconomy. In the latest research which is moving toward Genetically Modified Organisms (GMOs) that can perform multiple tasks at once. Using CRISPR-Cas9, scientists have been able to ceate “super-microbes” that can: • Detect a specific pollutant (like a biosensor). • Break down that pollutant (bioremediation). • Synthesize a fuel molecule (valorization) simultaneously. There is the need to produce biofuels more sustainably than the traditional way with the use of synthetic biology. The problem in Kenya right now we have a lot of second hand clothes that are piled up as waste in dump site, also plastics chocking waterways and scattered all over the streets with no central place to collect them or few collection centers. E-Waste where Kenya generates over 53,000 tonnes annually creating a waste problem. The new technology from synthetic biology would help to eradicate the problem and at the same time generate energy that will help counter the large import bill for gasoline, diesel and kerosine we purchase every year.

The research being done on biological “waste-to-fuel” systems has now led to a major shift from laboratory “proof of concept” to integrated biorefineries where organisms don’t just clean the environment,they act as the living hardware for fuel production. The discovery of a technology through research of Microbial Fuel Cell has made it possible to turn waste into electricity or hydrogen directly without burning anything which is being piloted in waste water plants. Used clothes wastes from Gikomba and Dandora can be turned into Bioethanol, Sewage & Heavy metals from Nairobi River can be turned into Biofuels, Hydrogen or electricity, Plastics in the creation of Bio-oil, Organic waste producing Biomethane. Since the GMO organisms will be bioengineerd to scout for the waste in different damp sites there would be the need to ensure the environment around the site is protected, with the technology being used to make sure the community around benefit from it and have the area restored and once done the organisms can be engineered to sense the completion of there task and intergrate into the ecosystem without harming it.

Environment • How would the damp site be free of the bioengineered organisms after conversion to biofuel? • How will the biofuel be evacuated fron the damp site without harming the ecosystem?

Equitable use of technology • Will the GMO be made available to the public? • How will the technology be used in the area where it is needed and will the community benefit from the biofuel?

Biosecurity • The technology needs to be safe to handle and use without leading to biological disasters. • The GMO should not be able to mutate and create a situation where they alter other organisms in the ecosystem.

Looking at the three different potential governance “actions” with the four aspects below (Purpose, Design, Assumption, Risks of Failure & “Success”)

Researchers • There is the need to show the standards and how the super microbes will be handled and produced either locally or imported. • Publications from reputable institutions to show how they are able to use similar microbes in a safe way with a manual compiled for the laboratory use of them. • A database that has all the known super microbes that are able to produce and how to be handled, the risks and best practices.

Microbiome Companies • There needs to be a way the regulators can look into auditing where the companies are following the law and standards set by researchers. • Public participation is needed to educate the community in the areas where they plan to use their technology. • The need to be informed each stage on what is going on with the project once it commences till the end.

Government Regulators • The agencies tasked to monitor will assess using their standards and gauge on what needs to be done with the super microbes registered on the quantities used. • Always monitoring that safety is observed for the audits conducted abruptly without notice to ensure safety of the products they claim to use and guidelines set. • Ensure the people on the site working are of the recommended number and not overcrowded and following ecological standards and public participation.

The researchers would be the laboratories that test and develop the microbes either in an institution like a University or private entity. The Microbiome companies design the microbiomes and have organisnm engineers who develop new organism using biology,they vary in size from small to large scale. The government regulators look into getting approvals and can use third party firms to enforce the regulations. To get approval in the use of synthetic genomics there are three primary regulators with the process streamlined under the 2022 Genome editing guidelines. The first step is The National Biosafety Authority (NBA) where one gets the permit from for the lab research and the risk assessment. The second step is National Environment Management Authority (NEMA) for the environmental impact assessment and the need for a permit to discharge the treated byproduct and bioprospecting permit for microbes. The third step is The Energy & Petroleum Regulatory Authority(EPRA) where you get the Biofuel production license, Construction Permit and KEBS standardization. The economic risks would be the bioavailability of plastic as an engineered microbe cannot eat a plastic bottle unless it is shredded and pretreated (Hydrothermal pretreatment). With the introduction of Carbon Credits by the Kenyan governmet in the Climate Change Act,can lead to a saving of 30% towards the operational costs.
Based on the scoring above the goverment would need to know how the super microbes function and have the community know the benefits of the use of them in clearing the waste. The Researchers and Microbiome companies need to return at least 5-10 % of the biofuels as a way for giving back to the community so as to minimize pushback. Since there is the incentive offered by the government on the use of local microbe,research can be done to see how they can be engineered to reduce the initial set up cost.

Homework Answers for Professor Jacobson

1. Nature’s machinery for copying DNA is called polymerase.

   What is the error rate of polymerase? The error rate of polymerase is 1 mistake per 10*6 base pairs.

   How does this compare to the length of the human genome? DNA polymerase which is an enzyme is approximately 10 – 15 nanometers (nm) in length while the human genome which is the template is approximately 2 meters when stretched out. A scale ratio of 1: 108 x longer (200m /10 nm = 20,000,000x)

   How does biology deal with that discrepancy? It does this by not relying on a single enzyme. It uses a highly organized, factory-like system with four key strategies: The first is the multiple origins of replication instead of a single one, each origin has two replication forks creating a replication bubble where thousands of DNA polymerase molecules can work simultaneously across all chromosomes where each has a small copying segment.

   The second, is it doesn’t work alone as is part of a complex of proteins known as a replisome a key component is the sliding clamp (PCNA in humans) where the donut- shaped protein encircles the DNA and tethers the polymerase to the template which leads to the increase in processivity making one polymerase can be able to add thousands of nucleotides without falling off, turning it from a slow inefficient enzyme to a high speed, long distance replicator.

   Third, different polymerases have specialized roles with the leading strand being synthesized continuously by a highly processive polymerase and the lagging strand synthesized in short Okazaki fragments that require different coordinated processes. The main replicative polymerases have proofreading ability (3’→5’ exonuclease activity). Where they are able to mmediately back up and fix a mistake, ensuring speed doesn’t come at the cost of catastrophic error rates (final error rate: ~1 in 10 billion bases)

   Fourth, the compartmentalization and packaging of DNA has mabe the 2 meters of DNA not in a loose tangle. It istightly wound and packaged with proteins into chromosomes inside the nucleus (~10 µm wide) where the replisome has to navigate this dense chromatin structure, with the Helicases unwinding it, topoisomerases relieve twisting stress, and other proteins modify the packaging to allow access. This organization makes it possible to bring distant genomic regions into physical proximity while making the logistics of finding origins and assembling machinery more efficient.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest? The different ways to code an average human protein is 10*214 , which is a very large number and impractical. Most of the codes fail in producing a functional protein in the living cell. The reason why most of the codes don’t work is not all of them are created equal where codon usage bias can affect translaton speed leading to misfolded or incomplete proteins. The second, is where mRNA has a secondary structure where it folds back on itself and form shapes like hair pins and loops and if a randomly chosen DNA sequence creates an mRNA strand that folds at the beginning, the ribosome can’t get on the track and start reading. The third is splicing (cutting and pasting) RNA before it is translated where specific sequences that signal where an intron (junk DNA) finishes ans an exon(coding DNA) starts, with many containing the code for a “splice-site” in the middle of the gene and throw away, leaving a fragment which is a useless protein. The fourth, DNA is chemically stable cause of the G-C pairs, where it is held together by hydrogen bonds, G-C pairs have three bonds, while A-T pairs only have two. Where too high of a G-C content the DNA ‘zipper’ would be really hard to open whereas a low A-T rich would make it unstable. The sixth is the human immune system through evolution, recognizes the Cytosine followed by a Guanine (CpG) as a pattern of a viral or bacterial infection and if the code has too many of these “CpG islands” triggers the cell that its under attack by a virus and could lead to gene silencing or an inflammatory response to “shield” itself.

Homework Answers for Dr. LeProust

1. What’s the most commonly used method for oligo synthesis currently? The oligonucleotide synthesis is the phosphoramidite method, using thr Solid Phase Synthesis (SPS). Naturally DNA builds in the 5’ to 3’ direction, while laboratory method builds the chain backward—from the 3’ end to the 5’ end. The method has a four step cycle, where it happens on a solid support (usually controlled-pore glass or polystyrene beads). The addition of a single nucleotide, the machine must complete a full revolution of these four chemical steps:

   The first step known as Deblocking (Deprotection) where the nucleotide is already attached to the solid support. Its 5’-hydroxyl group is “blocked” by a protective chemical called DMT (dimethoxytrityl) to prevent it from reacting prematurely. An acid is added to wash away the DMT, leaving a “naked” 5’- OH group ready for the next link. The second step whrere the next nucleotide (a phosphoramidite monomer) is added to the chamber along with an activator. The 5’ end of the growing chain binds to the 3’ end of the new monomer. Leading to an Efficiency usually >99%, but in chemistry, 100% is impossible. The third step is capping where a small percentage of the chains that failed to couple in Step 2, they must be “capped” (usually with acetic anhydride). This prevents them from reacting in future cycles, which would result in “deletion mutants”—strands that are missing a middle letter. The fourth step is oxidation where a bond formed during coupling is a bit unstable (a phosphite triester). An iodine solution is added to oxidize this bond, turning it into a stable phosphate triester which is the familiar backbone of DNA.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis? The phosphoramidite synthesis has a specific limit where errors accumulate with every step, making it hard to build a single strand longer than 200–300 bases with high purity. Since every time you add a base, there is the of lose a tiny bit of your starting material because the reaction never goes to 100% completion.

   Secondly, the Capping step of the cycle stops “failed” chains from growing further which leads to Purifying the “perfect” sequence away from the “almost perfect” ones becoming a nightmare. Comparing it to trying to find a specific grain of sand in a pile of slightly smaller grains of sand Thirdly, The first step of the cycle (Deblocking) uses an acid to remove the protective DMT group that DNA doesn’t actually like. Every time you expose the growing chain to acid, there is the risk of depurination where you accidentally snip a Guanine or Adenine base off the sugar backbone. If for comparison, a 200nt strand being subjected to the first base to 200 rounds of acid wash. By the time you reach the end, the beginning of your sequence is often chemically “chewed up.” Fourth, the time and mechanical failure as synthesizing 200 bases takes a long time (often 10–15 hours) and the longer the run, the higher the possibility of a “mechanical failure” where a bubble in the line, a slight drop in temperature, or a reagent running dry. Bringing about a failure at
   base 190 leading to wasting the entire 14-hour run and all the expensive chemicals used up to that point.

3. Why can’t you make a 2000bp gene via direct oligo synthesis? First, its statistically impossible even if your machine was the most efficient running 99.5% efficiency per base addition where only 0.004% of the molecules in your final mixture would actually be the correct 2,000bp length. The other 99.996% would be “truncated” sequence with broken fragments that are missing one or more bases.The massive mound of chemical errors makes it impossible to find the perfect bases.

   Second, there is the physical crowding and stuttering while the DNA chain grows to 2,000 bases, it doesn’t just stay a neat and organized. It starts to fold, tangle, and stick to the solid support (the glass or plastic bead it’s being built on). This leads to the “top” of the growing DNA chain becomes physically hard for new chemicals to reach because it’s buried in a crowd of other DNA strands. At the addition of the 2,000th base, the 1st base has been washed in acid 2,000 times making the chemical integrity of the beginning of the gene completely compromised.

Homework Answers for George Church

1. (Using Google & Prof . Church’s slide #4) What are the 10 essential amino acids in all animals and how does this affect my view of the “ Lysine Contigency”? Animals require 10 essential amino acids from their diet since they cannot synthesize them. These are universally needed across all animals like mammals, birds, and fish for protein synthesis and growth.

   Essential Amino Acids List The 10 essential ones, remembered by the acronym “PVT TIM HALL,” are:

   • Phenylalanine
   • Valine
   • Tryptophan
   • Threonine
   • Isoleucine
   • Methionine
   • Histidine
   • Arginine
   • Leucine
   • Lysine

   Lysine Contingency as explained in “Jurassic Park”, was the “lysine contingency” genetically modified dinosaurs to be unable to produce lysine, an essential amino acid, making them dependent on park-supplied supplements to prevent escape and survival in the wild. It failed scientifically as all animals, including dinosaurs as modeled, already couldn’t synthesize lysine and obtain it from protein-rich foods like meat or plants, abundant in ecosystems. Removing synthesis offers no control, as lysine is widespread, rendering the plan ineffective as dinosaurs would simply eat lysine- containing prey or vegetation.

References

1. Bioremediation of environmental wastes (https://www.frontiersin.org/journals/agronomy/articles/10.3389/fagro.2023.1183691/full)

2. Community Guide to Bioremediation (https://semspub.epa.gov/work/HQ/401583.pdf)

3. Global Situation of Bioremediation of Leachate-Contaminated (https://pmc.ncbi.nlm.nih.gov/articles/PMC10145224/) and (https://pmc.ncbi.nlm.nih.gov/articles/PMC11607652/)

4. Conversion of organic wastes into biofuel by microorganisms ( https://www.sciencedirect.com/science/article/pii/S2772801323000180)

5. (https://envaco.org/epr-in-kenya-legal-framework-and-policy-instruments/)

6. (https://envaco.org/e-waste-management-in-kenya-challenges-and-opportunities-in-the-digital-age/)

7. (https://nutrenaworld.com/blog/horses/what-are-essential-amino-acids-in-protein-and-why-do-they-matter/)

8. (https://jurassicpark.fandom.com/wiki/Lysine_contingency)

9. The use of LLM to help with finding information and reporting

Week 2: DNA READ, WRITE, AND EDIT

Week # 2 Homework

DNA READ, WRITE & EDIT

A look at the sequencing and synthesis workflows, restriction digests and gel electrophoresis, and early genome-editing frameworks.

Part 1: Benchling & In-silico Gel Art

See the Gel Art: Restriction Digests and Gel Electrophoresis protocol for details. Overview: • Make a free account at benchling.com • Import the Lambda DNA. • Simulate Restriction Enzyme Digestion with the following Enzymes: ◦ EcoRI ◦ HindIII ◦ BamHI ◦ KpnI ◦ EcoRV ◦ SacI ◦ SalI • Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks. • You might find Ronan’s website a helpful tool for quickly iterating on designs!

https://rcdonovan.com/gel-art

I was able to make a free account on Benchling and imported the Lamda DNA sequence as seen below.

A pattern below showing the simulation for each of the enzymes producing different fragment patterns created from the restriction enzyme digest with the following enzymes: ◦ EcoRI ◦ HindIII ◦ BamHI ◦ KpnI ◦ EcoRV ◦ SacI ◦ SalI

Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks.

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

The instructions on the lab experiment designed in Part 1 and outlined in the Gel Art: Restriction Digests and Gel Electrophoresis protocol. Since I had no access to the lab experiment, the simulation of the gel shows how Lamda DNA would have been digested by the seven different restriction enzymes as seen from the gel electrophoresis plate. The individual lanes show how each enzyme cut the DNA, using the NEB2-log as the ladder on the left as a reference for size. The patterns are used to verify the sequence and map the DNA.

Part 3: DNA Design Challenge

3.1 Choose your protein.

Protein: Amyloid beta precursor protein Organism: Homo sapiens GenBank: BDX53017.1 AA Sequence

BDX53017.1 amyloid beta precursor protein [Homo sapiens] MLPGLALLLLAAWTARALEVPTDGNAGLLAEPQIAMFCGRLNMHMNVQNGKWDSDPSGTKTCIDTKEGIL QYCQEVYPELQITNVVEANQPVTIQNWCKRGRKQCKTHPHFVIPYRCLVGEFVSDALLVPDKCKFLHQER MDVCETHLHWHTVAKETCSEKSTNLHDYGMLLPCGIDKFRGVEFVCCPLAEESDNVDSADAEEDDSDVWW GGADTDYADGSEDKVVEVAEEEEVAEVEEEEADDDEDDEDGDEVEEEAEEPYEEATERTTSIATTTTTTT ESVEEVVREVCSEQAETGPCRAMISRWYFDVTEGKCAPFFYGGCGGNRNNFDTEEYCMAVCGSAMSQSLL KTTQEPLARDPVKLPTTAASTPDAVDKYLETPGDENEHAHFQKAKERLEAKHRERMSQVMREWEEAERQA KNLPKADKKAVIQHFQEKVESLEQEAANERQQLVETHMARVEAMLNDRRRLALENYITALQAVPPRPRHV FNMLKKYVRAEQKDRQHTLKHFEHVRMVDPKKAAQIRSQVMTHLRVIYERMNQSLSLLYNVPAVAEEIQD EVDELLQKEQNYSDDVLANMISEPRISYGNDALMPSLTETKTTVELLPVNGEFSLDDLQPWHSFGADSVP ANTENEVEPVDARPAADRGLTTRPGSGLTNIKTEEISEVKMDAEFRHDSGYEVHHQKLVFFAEDVGSNKG AIIGLMVGGVVIATVIVITLVMLKKKQYTSIHHGVVEVDAAVTPEERHLSKMQQNGYENPTYKFFEQMQN

I chose this protein since numerous studies have placed the protein leading to a molecular pathway mechanism that leads to neurodegeneration, synaptic failure and the clinical onset of Alzheimer’s disease.

In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose. [Example from our group homework, you may notice the particular format — The example below came from UniProt]

sp|P03609|LYS_BPMS2 Lysis protein OS=Escherichia phage MS2 OX=12022 PE=2 SV=1 METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLL EAVIRTVTTLQQLLT

3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence.

Searching Online on how to Reverse Translate, I found the following website that has the tool to help me reverse translate: https://www.bioinformatics.org/sms2/rev_trans.html with the results shown in the image below.

I used this tool: https://www.bioinformatics.org/sms2/rev_trans.html

Reverse Translate results Results for 770 residue sequence “BDX53017.1 amyloid beta precursor protein [Homo sapiens]” starting “MLPGLALLLL”

reverse translation of BDX53017.1 amyloid beta precursor protein [Homo sapiens] to a 2310 base sequence of most likely codons. atgctgccgggcctggcgctgctgctgctggcggcgtggaccgcgcgcgcgctggaagtg ccgaccgatggcaacgcgggcctgctggcggaaccgcagattgcgatgttttgcggccgc ctgaacatgcatatgaacgtgcagaacggcaaatgggatagcgatccgagcggcaccaaa acctgcattgataccaaagaaggcattctgcagtattgccaggaagtgtatccggaactg cagattaccaacgtggtggaagcgaaccagccggtgaccattcagaactggtgcaaacgc ggccgcaaacagtgcaaaacccatccgcattttgtgattccgtatcgctgcctggtgggc gaatttgtgagcgatgcgctgctggtgccggataaatgcaaatttctgcatcaggaacgc atggatgtgtgcgaaacccatctgcattggcataccgtggcgaaagaaacctgcagcgaa aaaagcaccaacctgcatgattatggcatgctgctgccgtgcggcattgataaatttcgc ggcgtggaatttgtgtgctgcccgctggcggaagaaagcgataacgtggatagcgcggat gcggaagaagatgatagcgatgtgtggtggggcggcgcggataccgattatgcggatggc agcgaagataaagtggtggaagtggcggaagaagaagaagtggcggaagtggaagaagaa gaagcggatgatgatgaagatgatgaagatggcgatgaagtggaagaagaagcggaagaa ccgtatgaagaagcgaccgaacgcaccaccagcattgcgaccaccaccaccaccaccacc gaaagcgtggaagaagtggtgcgcgaagtgtgcagcgaacaggcggaaaccggcccgtgc cgcgcgatgattagccgctggtattttgatgtgaccgaaggcaaatgcgcgccgtttttt tatggcggctgcggcggcaaccgcaacaactttgataccgaagaatattgcatggcggtg tgcggcagcgcgatgagccagagcctgctgaaaaccacccaggaaccgctggcgcgcgat ccggtgaaactgccgaccaccgcggcgagcaccccggatgcggtggataaatatctggaa accccgggcgatgaaaacgaacatgcgcattttcagaaagcgaaagaacgcctggaagcg aaacatcgcgaacgcatgagccaggtgatgcgcgaatgggaagaagcggaacgccaggcg aaaaacctgccgaaagcggataaaaaagcggtgattcagcattttcaggaaaaagtggaa agcctggaacaggaagcggcgaacgaacgccagcagctggtggaaacccatatggcgcgc gtggaagcgatgctgaacgatcgccgccgcctggcgctggaaaactatattaccgcgctg caggcggtgccgccgcgcccgcgccatgtgtttaacatgctgaaaaaatatgtgcgcgcg gaacagaaagatcgccagcataccctgaaacattttgaacatgtgcgcatggtggatccg aaaaaagcggcgcagattcgcagccaggtgatgacccatctgcgcgtgatttatgaacgc atgaaccagagcctgagcctgctgtataacgtgccggcggtggcggaagaaattcaggat gaagtggatgaactgctgcagaaagaacagaactatagcgatgatgtgctggcgaacatg attagcgaaccgcgcattagctatggcaacgatgcgctgatgccgagcctgaccgaaacc aaaaccaccgtggaactgctgccggtgaacggcgaatttagcctggatgatctgcagccg tggcatagctttggcgcggatagcgtgccggcgaacaccgaaaacgaagtggaaccggtg gatgcgcgcccggcggcggatcgcggcctgaccacccgcccgggcagcggcctgaccaac attaaaaccgaagaaattagcgaagtgaaaatggatgcggaatttcgccatgatagcggc tatgaagtgcatcatcagaaactggtgttttttgcggaagatgtgggcagcaacaaaggc gcgattattggcctgatggtgggcggcgtggtgattgcgaccgtgattgtgattaccctg gtgatgctgaaaaaaaaacagtataccagcattcatcatggcgtggtggaagtggatgcg gcggtgaccccggaagaacgccatctgagcaaaatgcagcagaacggctatgaaaacccg acctataaattttttgaacagatgcagaac

reverse translation of BDX53017.1 amyloid beta precursor protein [Homo sapiens] to a 2310 base sequence of consensus codons. atgytnccnggnytngcnytnytnytnytngcngcntggacngcnmgngcnytngargtn ccnacngayggnaaygcnggnytnytngcngarccncarathgcnatgttytgyggnmgn ytnaayatgcayatgaaygtncaraayggnaartgggaywsngayccnwsnggnacnaar acntgyathgayacnaargarggnathytncartaytgycargargtntayccngarytn carathacnaaygtngtngargcnaaycarccngtnacnathcaraaytggtgyaarmgn ggnmgnaarcartgyaaracncayccncayttygtnathccntaymgntgyytngtnggn garttygtnwsngaygcnytnytngtnccngayaartgyaarttyytncaycargarmgn atggaygtntgygaracncayytncaytggcayacngtngcnaargaracntgywsngar aarwsnacnaayytncaygaytayggnatgytnytnccntgyggnathgayaarttymgn ggngtngarttygtntgytgyccnytngcngargarwsngayaaygtngaywsngcngay gcngargargaygaywsngaygtntggtggggnggngcngayacngaytaygcngayggn wsngargayaargtngtngargtngcngargargargargtngcngargtngargargar gargcngaygaygaygargaygaygargayggngaygargtngargargargcngargar ccntaygargargcnacngarmgnacnacnwsnathgcnacnacnacnacnacnacnacn garwsngtngargargtngtnmgngargtntgywsngarcargcngaracnggnccntgy mgngcnatgathwsnmgntggtayttygaygtnacngarggnaartgygcnccnttytty tayggnggntgyggnggnaaymgnaayaayttygayacngargartaytgyatggcngtn tgyggnwsngcnatgwsncarwsnytnytnaaracnacncargarccnytngcnmgngay ccngtnaarytnccnacnacngcngcnwsnacnccngaygcngtngayaartayytngar acnccnggngaygaraaygarcaygcncayttycaraargcnaargarmgnytngargcn aarcaymgngarmgnatgwsncargtnatgmgngartgggargargcngarmgncargcn aaraayytnccnaargcngayaaraargcngtnathcarcayttycargaraargtngar wsnytngarcargargcngcnaaygarmgncarcarytngtngaracncayatggcnmgn gtngargcnatgytnaaygaymgnmgnmgnytngcnytngaraaytayathacngcnytn cargcngtnccnccnmgnccnmgncaygtnttyaayatgytnaaraartaygtnmgngcn garcaraargaymgncarcayacnytnaarcayttygarcaygtnmgnatggtngayccn aaraargcngcncarathmgnwsncargtnatgacncayytnmgngtnathtaygarmgn atgaaycarwsnytnwsnytnytntayaaygtnccngcngtngcngargarathcargay gargtngaygarytnytncaraargarcaraaytaywsngaygaygtnytngcnaayatg athwsngarccnmgnathwsntayggnaaygaygcnytnatgccnwsnytnacngaracn aaracnacngtngarytnytnccngtnaayggngarttywsnytngaygayytncarccn tggcaywsnttyggngcngaywsngtnccngcnaayacngaraaygargtngarccngtn gaygcnmgnccngcngcngaymgnggnytnacnacnmgnccnggnwsnggnytnacnaay athaaracngargarathwsngargtnaaratggaygcngarttymgncaygaywsnggn taygargtncaycaycaraarytngtnttyttygcngargaygtnggnwsnaayaarggn gcnathathggnytnatggtnggnggngtngtnathgcnacngtnathgtnathacnytn gtnatgytnaaraaraarcartayacnwsnathcaycayggngtngtngargtngaygcn gcngtnacnccngargarmgncayytnwsnaaratgcarcaraayggntaygaraayccn acntayaarttyttygarcaratgcaraay

The Central Dogma discussed in class and recitation describes the process in which DNA sequence becomes transcribed and translated into protein. The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from. Using one of the tools discussed in class, NCBI or online tools (google “reverse translation tools”), determine the nucleotide sequence that corresponds to the protein sequence you chose above.

Lysis protein DNA sequence atggaaacccgattccctcagcaatcgcagcaaactccggcatctactaatagacgccggccattcaaacatgaggattacccatgtcgaagacaacaaagaagttcaactctttatgtattgatcttcctcgcgatctttctctcgaaatttac caatcaattgcttctgtcgctactggaagcggtgatccgcacagtgacgactttacagcaattgcttacttaa

3.3. Codon optimization.

Once a nucleotide sequence of your protein is determined, you need to codon optimize your sequence. You may, once again, utilize google for a “codon optimization tool”. In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why? It is done to come up with codons that are more frequently used by the host organism so as it can efficiently translate the protein and produce the desired protein in higher levels. As codon that are rare or less favoured would affect the production of the protein in the organism and the programs helps in identifying the best fit. I chose E.coli cause it is a cheaper alternative in terms of cost and is widely used. I also the use of human to use in the trial of the protein in a mammalian system.

Improved DNA homo sapiens

I searched online for ways to Codon Optimize and used the tool: https://en.vectorbuilder.com/tool/codon-optimization.html

Improved E.coli

Improved DNA[1]: GC=58.01%, CAI=0.92 ATGCTGCCTGGACTGGCCCTCCTGCTGCTGGCCGCCTGGACCGCCAGGGCCCTGGAGGTGCCCACCGACGGTAACGCCGGCCTGCTGGCCGAGCCCCAGATCGCTATGTTCTGCGGGAGGCTGAACATGCACATGAACGTGCAGAACGGGA AGTGGGACTCCGACCCCTCCGGAACTAAGACTTGCATCGACACAAAAGAGGGAATTCTGCAGTACTGTCAGGAAGTGTACCCCGAGCTGCAGATCACTAATGTGGTGGAAGCTAATCAGCCAGTCACAATCCAGAACTGGTGCAAGAGAGGC AGAAAGCAGTGCAAAACCCACCCCCACTTCGTGATCCCATACAGGTGCCTGGTGGGCGAGTTCGTCTCTGACGCTCTGCTGGTGCCTGACAAATGCAAGTTCCTCCATCAGGAGAGGATGGACGTTTGTGAGACACACCTCCACTGGCACACC GTCGCCAAAGAGACATGCTCTGAGAAGAGTACTAACCTGCACGACTATGGGATGCTGCTGCCTTGTGGGATTGACAAGTTCCGGGGCGTGGAGTTCGTGTGCTGTCCCCTGGCCGAGGAGAGCGACAATGTGGACAGTGCCGACGCCGAGGAAG ACGACAGCGACGTGTGGTGGGGCGGCGCCGACACCGACTACGCCGATGGGAGCGAGGATAAAGTCGTGGAAGTCGCCGAGGAGGAGGAGGTGGCCGAGGTGGAGGAAGAAGAGGCCGACGACGATGAGGACGACGAGGATGGCGACGAGGTGGA GGAGGAAGCTGAGGAGCCATATGAAGAGGCAACAGAGCGGACCACCTCTATTGCGACCACAACCACCACCACCACTGAGAGCGTGGAGGAGGTGGTGAGGGAAGTGTGCTCTGAACAGGCCGAAACCGGGCCATGTAGAGCTATGATCTCCAGA TGGTATTTCGACGTCACAGAGGGCAAGTGCGCCCCCTTCTTTTACGGCGGCTGTGGCGGGAACCGGAACAATTTTGATACTGAGGAGTACTGCATGGCCGTCTGCGGCTCTGCAATGAGCCAGTCCCTGCTTAAAACTACCCAGGAGCCCCTGG CCAGGGACCCTGTGAAACTGCCCACCACCGCAGCCTCTACTCCCGATGCCGTGGACAAGTACCTGGAAACCCCCGGAGATGAGAACGAGCATGCCCACTTTCAGAAGGCAAAGGAAAGACTGGAGGCCAAGCACCGCGAAAGAATGTCCCAGGT GATGAGGGAATGGGAAGAGGCCGAGCGCCAGGCCAAGAACCTGCCCAAAGCCGACAAGAAGGCCGTGATCCAGCACTTTCAGGAAAAGGTGGAGTCTCTGGAGCAGGAGGCCGCCAATGAGAGACAGCAGCTGGTGGAGACCCACATGGCCCGC GTCGAGGCCATGCTGAATGACAGAAGGCGGCTGGCCCTGGAGAACTACATCACAGCCCTGCAGGCTGTGCCACCAAGGCCCAGGCATGTGTTTAACATGCTGAAAAAGTACGTGAGAGCCGAACAGAAGGATAGGCAGCACACACTGAAACATT TTGAGCACGTGCGGATGGTGGACCCCAAGAAGGCTGCACAGATCAGGTCTCAGGTGATGACCCATCTTAGAGTCATATACGAGAGAATGAACCAGTCCCTGAGCCTGCTGTATAACGTGCCCGCCGTGGCCGAGGAGATCCAGGACGAGGTGGA TGAGCTGCTGCAGAAGGAGCAGAATTATAGTGACGATGTGCTGGCCAACATGATCTCCGAGCCAAGAATCTCCTACGGAAACGACGCACTGATGCCCAGCCTGACCGAGACAAAGACAACAGTGGAGCTGCTGCCAGTGAATGGCGAATTTTCC CTGGACGATCTGCAGCCTTGGCACTCATTCGGCGCCGATAGCGTCCCTGCCAACACAGAGAACGAAGTGGAGCCTGTGGACGCCCGGCCTGCCGCAGACAGGGGCCTGACCACTAGGCCAGGATCCGGCCTGACCAACATCAAAACCGAGGAGA TCTCCGAGGTGAAGATGGATGCCGAGTTCAGACACGACAGCGGATACGAGGTGCACCACCAGAAGCTGGTGTTCTTTGCCGAGGATGTGGGAAGCAACAAGGGCGCAATCATCGGTCTGATGGTGGGCGGCGTGGTGATCGCCACCGTGATCGT GATCACCCTGGTGATGCTGAAAAAGAAGCAGTATACATCTATCCACCACGGCGTGGTGGAGGTGGATGCCGCCGTGACCCCCGAAGAGAGGCACCTGAGCAAGATGCAGCAGAACGGGTATGAGAATCCCACTTACAAATTCTTTGAGCAGATG CAGAAC

Improved DNA[1]: GC=53.81%, CAI=0.93 (Escherichia Coli) ATGCTGCCGGGCCTGGCGCTGCTGCTGCTGGCGGCGTGGACCGCGCGCGCGCTGGAAGTGCCGACCGACGGCAATGCGGGCCTGCTGGCCGAACCGCAGATTGCCATGTTTTGCGGCCGCCTGAATATGCATATGAACGTGCAGAATGGCAAAT GGGATAGCGATCCGAGCGGCACCAAAACGTGCATTGATACCAAAGAAGGCATTCTGCAGTACTGTCAGGAAGTGTATCCGGAACTGCAGATCACCAATGTGGTGGAAGCGAACCAGCCGGTGACCATTCAGAACTGGTGCAAACGCGGCCGCAA ACAGTGTAAAACCCATCCGCATTTTGTGATTCCGTATCGTTGCCTGGTGGGCGAGTTCGTTAGCGATGCCCTGCTGGTGCCGGATAAATGCAAATTTCTGCATCAGGAACGCATGGATGTGTGCGAAACCCATCTGCATTGGCATACTGTTGCA AAAGAAACCTGCTCAGAAAAAAGCACCAACCTGCATGATTATGGCATGCTGCTGCCGTGCGGCATTGATAAATTTCGCGGTGTTGAATTTGTGTGCTGCCCGCTGGCGGAAGAAAGCGATAACGTGGATAGTGCAGATGCGGAAGAAGATGACA GCGATGTGTGGTGGGGCGGCGCGGATACCGATTATGCGGACGGCAGCGAAGATAAAGTTGTGGAAGTGGCGGAGGAAGAAGAAGTGGCAGAAGTGGAAGAAGAAGAAGCCGATGATGATGAAGATGATGAAGATGGCGATGAAGTTGAAGAAGA AGCGGAAGAACCGTATGAAGAAGCGACGGAACGCACCACCAGCATTGCCACCACCACGACCACGACCACCGAAAGCGTGGAAGAAGTGGTGCGTGAAGTGTGCAGCGAACAGGCGGAAACCGGGCCGTGTCGTGCCATGATTAGCCGCTGGTAT TTTGATGTTACCGAAGGTAAATGCGCGCCGTTTTTTTATGGCGGCTGCGGTGGCAATCGTAACAACTTTGATACCGAAGAATACTGCATGGCCGTTTGCGGCAGCGCAATGTCGCAGAGCCTGCTGAAAACCACCCAGGAACCGCTGGCGCGCG ACCCGGTGAAACTGCCGACCACCGCAGCCAGCACCCCGGATGCCGTTGATAAATACCTGGAAACCCCGGGTGATGAAAATGAACATGCGCATTTTCAGAAAGCCAAAGAACGCCTGGAAGCGAAACATCGTGAACGCATGAGCCAGGTGATGCG CGAATGGGAAGAAGCGGAACGTCAGGCGAAAAACCTGCCGAAAGCGGACAAAAAGGCCGTGATTCAGCACTTTCAGGAGAAAGTGGAAAGCCTGGAGCAGGAAGCGGCCAATGAACGTCAGCAGCTGGTAGAAACCCACATGGCGCGCGTGGAA GCCATGCTGAACGATCGCCGTCGCTTGGCGCTGGAAAACTACATTACCGCGCTGCAGGCGGTGCCGCCGCGCCCGCGCCATGTGTTTAATATGCTGAAAAAATATGTGCGCGCCGAACAGAAAGATCGTCAGCACACCCTGAAACATTTTGAAC ACGTGCGCATGGTAGATCCGAAAAAAGCGGCACAGATTCGTAGCCAAGTGATGACCCACCTTCGCGTGATTTACGAACGCATGAACCAGAGCCTGAGCCTGCTGTATAACGTGCCGGCAGTGGCGGAAGAAATTCAGGATGAAGTGGACGAATT ACTGCAGAAAGAACAAAATTACAGCGATGATGTGCTGGCCAACATGATTTCGGAACCGCGCATTAGCTACGGCAATGATGCCCTGATGCCGAGCCTGACCGAAACCAAAACCACCGTTGAACTGCTGCCGGTAAATGGCGAATTCAGCCTGGAT GATCTGCAGCCGTGGCATAGCTTTGGCGCGGATAGCGTGCCGGCAAACACCGAAAATGAAGTTGAACCGGTGGATGCCCGTCCGGCGGCCGATCGTGGCCTGACGACCCGTCCGGGCAGCGGTCTGACCAACATTAAAACCGAAGAAATTAGCG AAGTGAAAATGGATGCGGAATTTCGCCACGATAGCGGCTATGAAGTGCACCATCAGAAACTGGTGTTCTTTGCGGAAGATGTGGGCAGTAACAAAGGCGCGATTATTGGCCTGATGGTGGGCGGCGTGGTAATCGCGACCGTCATTGTGATTAC CCTGGTGATGCTGAAAAAAAAACAGTATACCAGCATTCACCATGGCGTGGTGGAAGTGGATGCCGCAGTTACCCCGGAAGAACGCCATCTGAGCAAAATGCAGCAGAACGGCTACGAAAATCCGACCTATAAATTCTTTGAACAGATGCAGAAT

Lysis protein DNA sequence with Codon-Optimization ATGGAAACCCGCTTTCCGCAGCAGAGCCAGCAGACCCCGGCGAGCACCAACCGCCGCCGCCCGTTCAAACATGAAGATTATCCGTGCCGTCGTCAGCAGCGCAGCAGCACCCTGTATGTGCTGATTTTTCTGGCGATTTTTCTGAGCAAATTCA CCAACCAGCTGCTGCTGAGCCTGCTGGAAGCGGTGATTCGCACAGTGACGACCCTGCAGCAGCTGCTGACCTAA

3.4. You have a sequence! Now what?

What technologies could be used to produce this protein from your DNA? Describe in your words the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both. Cells rely on DNA as an instruction manual for protein synthesis. The DNA sequence is first transcribed into messenger RNA (mRNA), which acts as a working blueprint. The cell then translates this mRNA template step-by-step to assemble the desired protein.

The production of amyloid beta precursor protein (APP) follows these standard genetic expression processes. Interestingly, a single APP gene can generate several different protein variants through a mechanism called alternative splicing, which occurs during and after transcription. When scientists create recombinant APP in laboratory settings, they must carefully manage multiple factors—including how much protein is produced, whether it folds correctly, and how chemical modifications are added after the protein is initially made. One particularly important modification is glycosylation (the addition of sugar molecules). These chemical alterations are essential because they affect how APP sits in the cell membrane and how it gets broken down into amyloid-beta (Aβ), a protein fragment associated with Alzheimer’s disease.

Central Dogma Process DNA encoding APP is transcribed into pre-mRNA by RNA polymerase II, which includes exons and introns. Alternative splicing (e.g., inclusion/exclusion of exon 15) generates multiple mature mRNA isoforms, which ribosomes then translate into distinct APP protein variants (e.g., APP695, APP751, APP770) differing in length and function.

Cell-Dependent Methods HEK293 or CHO cells are optimal for mammalian expression due to proper folding and secretion. Transfect with pcDNA3.1-APP plasmids under CMV promoter, induce with IPTG if hybrid, and purify via Ni-NTA (His-tagged) or immunoprecipitation; yields reach 10-50 mg/L with glycosylation intact for secretase cleavage studies.

Cell-Free Methods Rabbit reticulocyte lysate or wheat germ extracts excel for rapid prototyping. Mix PCR-amplified APP DNA (T7 promoter) with lysate, Mg2+/NTPs, and translate in 1-2 hours; add microsomes for membrane insertion. Best for isotopic labeling (15N-APP) without cellular toxicity, yielding 1-5 μg/μL but lacking full glycosylation.

Recommendation For authentic APP with Aβ-processing fidelity, use HEK293 cell-dependent systems over cell-free, as they support splicing machinery and PTMs essential for multiple isoforms. Cell-free suits quick screening or labeled protein.

Cell-Dependent Methods HEK293 and CHO cells are the preferred choice for producing APP proteins in mammalian systems because they naturally promote proper protein folding and enable the release of proteins from cells. Researchers introduce APP genes into these cells using pcDNA3.1 plasmids controlled by the CMV promoter. If using a hybrid system, IPTG can trigger protein production. Once synthesized, the APP protein is isolated using purification techniques like Ni-NTA chromatography (which targets His-tags) or immunoprecipitation. This approach produces substantial quantities—between 10-50 mg/L—and crucially, the proteins retain their sugar modifications (glycosylation), which are necessary for studying how secretase enzymes break down APP into amyloid-beta.

Cell-Free Methods Cell-free protein synthesis offers a faster, simpler alternative using extracts from rabbit reticulocytes or wheat germ instead of living cells. Scientists amplify APP DNA (using T7 promoter sequences) and combine it with the cell extract along with magnesium and nucleotides, completing protein synthesis in just 1-2 hours. Adding microsomes (membrane fragments) allows the newly made protein to insert into a membrane-like environment. This method is ideal for rapidly testing concepts and for creating labeled proteins enriched with isotopes like 15N without harming cells. However, yields are more modest at 1-5 μg/μL, and the proteins don’t receive the complete sugar modifications that cellular systems provide.

Recommendation For studying APP that behaves authentically and processes into amyloid-beta correctly, cell-based systems using HEK293 cells are superior to cell-free approaches. This is because living cells contain the machinery to perform alternative splicing and add critical chemical modifications—processes essential for generating the multiple APP variants. Cell-free systems work best for quick preliminary experiments or when producing specialized labeled proteins.

References

1. Amyloid Precursor Protein Processing and Bioenergetics - PMC (https://pmc.ncbi.nlm.nih.gov/articles/PMC5316384/)
2. Amyloid beta: structure, biology and structure-based therapeutic …- (https://www.nature.com/articles/aps201728)
3. Knockdown of Amyloid Precursor Protein: Biological … - (https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.835645/full)
4. Amyloid-beta precursor protein - (https://en.wikipedia.org/wiki/Amyloid-beta_precursor_protein)
5. Recombinant amyloid beta-peptide production by coexpression with an affibody ligand - (https://pmc.ncbi.nlm.nih.gov/articles/PMC2606684/)
6. Can a single gene produce multiple proteins? - (https://scienceofbiogenetics.com/articles/investigating-the-phenomenon-can-a-single-gene-produce-multiple-protein-variations)
7. Targeting Amyloid-β Precursor Protein, APP, Splicing with Antisense Oligonucleotides Reduces Toxic Amyloid-β Production- (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5986716/)
8. A Cellular Model of Amyloid Precursor Protein Processing and Amyloid-β Peptide Production - (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3931259/)
9. Differential Processing of Amyloid-β Precursor Protein Directs Human Embryonic Stem Cell Proliferation and Differentiation into Neuronal Precursor Cells-(https://pmc.ncbi.nlm.nih.gov/articles/PMC2749153/)
10. Translation: DNA to mRNA to Protein - (https://www.nature.com/scitable/topicpage/translation-dna-to-mrna-to-protein-393/)
11. Targeting Amyloid-β Precursor Protein, APP, Splicing…- PMC (https://pmc.ncbi.nlm.nih.gov/articles/PMC5986716/)
12. Genetic and chemical disruption of amyloid precursor protein… - PMC (https://pmc.ncbi.nlm.nih.gov/articles/PMC10839650/)
13. A novel method for expression and purification of authentic amyloid-β with and without 15 N labels- (https://www.sciencedirect.com/science/article/abs/pii/S1046592815001072)
14. Transcription: an overview of DNA transcription (article) - (https://www.khanacademy.org/science/ap-biology/gene-expression-and-regulation/transcription-and-rna-processing/a/overview-of-transcription)
15. Targeting Amyloid-β Precursor Protein, APP, Splicing with… (https://www.merckmillipore.com/SI/en/tech-docs/paper/1325529)

Part 4: Prepare a Twist DNA Synthesis Order

This is a practice exercise, not necessarily your real Twist order!

4.1. Create a Twist account, and Benchling account

4.2. Build Your DNA Insert Sequence

For example, let’s make a sequence that will make E. coli glow fluorescent green under UV light by constitutively (always) expressing sfGFP (a green fluorescent protein):

4.6. Choose Your Vector

For this demonstration, choose a Twist cloning vectors like pTwist Amp High Copy.

Go back to your Benchling account. Inside of a folder, click the import DNA/RNA sequence button and upload the GenBank file you just downloaded.

This is the plasmid you just built with your expression cassette included. Congratulations on building your first plasmid!

Part 5: DNA Read/Write/Edit

5.1 DNA Read

(i) What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank). I wanted to research on the effects of microplastics and nanoplastics to our DNA as a lot of diseases are in the population and there is a possibility plastics in our environment could be contributing. I used Large Language Models to help me get the information. The detection of plastics particles is done through spectroscopic ways with methods like Raman or FTIR(Fourier-Transform Infrared,while sequencing methods won’t be able to physically detect plastics. I am curious to find out the research on whether it is true plastics are affecting DNA and other body functions.

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?

Next-Generation Sequencing for Microplastic Research Advanced sequencing technologies, particularly Illumina’s second-generation platforms, enable researchers to assess the genetic damage, health impacts, and changes in gene activity triggered by exposure to microplastics and nanoplastics. Each approach requires distinct preparation procedures, specific chemical processes during sequencing, and generates different types of data. The core technique relies on synthesis-based sequencing, which simultaneously reads millions of short DNA segments with exceptional accuracy. These methods reveal molecular differences and distinct mutation patterns in DNA, helping scientists identify whether cells have been damaged or exposed to harmful plastics. The main sequencing approaches detailed below measure how micro- and nanoplastics affect living organisms:

Key Sequencing Technologies Whole Genome Sequencing (WGS): This method scans the entire genetic code to identify point mutations and large-scale chromosomal rearrangements (including single base changes, paired base alterations, and insertions/deletions) that result from exposure to toxic compounds released by plastics, such as Bisphenol A (BPA). Metagenomic Sequencing (16S/ITS): This technique examines changes in bacterial populations living in the human gut and identifies genes that allow microorganisms to break down plastic particles after they are consumed. RNA-Seq Technologies (Standard and Low-Input): These approaches measure the abundance of thousands of messenger RNA molecules across an entire genome, revealing how microplastic exposure alters which genes are active in tissues and laboratory-grown cell structures. Single-Cell RNA Sequencing (scRNA-seq): This specialized method examines individual cells within the kidney and liver, providing a detailed snapshot of genetic activity in specific cell types, including immune cells and kidney filtration cells, after plastic exposure. ChIP-Seq: This technique identifies where proteins bind to DNA, enabling researchers to determine whether observed changes in gene activity result directly from signaling pathways or arise indirectly from the cell’s stress response.

Workflow for Next-Generation Sequencing Technologies Implementing these sequencing technologies requires a structured, multi-step procedure that begins with preparing biological samples and concludes with computational analysis of the results.

Sample Preparation and Cell Isolation The first stage involves preparing biological tissue samples for analysis. Tissues—such as kidney samples from laboratory mice—are broken down into individual cells using automated equipment. When researchers need to study single cells in isolation, they employ microfluidic devices, which are miniaturized systems capable of sorting and capturing individual cells with precision.

RNA/DNA Extraction and Quality Control High-quality genetic material must be isolated from the prepared samples before sequencing can begin. For projects involving RNA, the extracted material must meet strict quality standards; specifically, the RNA Integrity Number (RIN) should be 8 or higher to ensure that the RNA molecules remain intact and suitable for accurate analysis.

Library Construction This critical step prepares the genetic material for sequencing. The DNA or RNA is fragmented into manageable pieces, and for RNA-based studies, reverse transcription converts RNA back into DNA. Adapter sequences and unique identifying barcodes are then attached to each fragment, allowing researchers to sort and track the samples during and after sequencing.

Sequencing Run The prepared libraries are pooled together and loaded onto high-capacity sequencing instruments, such as the Illumina HiSeq X or NovaSeq 6000. These machines generate sequences of both ends of each DNA fragment (paired-end reads), producing massive amounts of genetic information in a single run.

Bioinformatics Analysis The raw sequencing data undergoes three distinct computational stages: Primary Analysis: The sequencing instrument produces raw data files in FASTQ format, which contain the DNA sequences alongside quality scores indicating how confident the machine is in each nucleotide reading. Secondary Analysis: The raw sequences are compared and aligned to a reference genome (such as the pig genome, Sus scrofa, or mouse genome, Mus musculus) using specialized software tools like HISAT2. Alternatively, researchers may assemble the sequences without a reference by comparing them to each other. Tertiary Analysis: The aligned data is processed to measure how actively each gene is being expressed—using metrics such as FPKM or TPM—and to identify which genes show significantly different activity levels between samples (Differentially Expressed Genes, or DEGs).

Data Outputs Revealing Molecular Damage from Plastic Exposure These advanced sequencing technologies generate comprehensive molecular information that maps the biological harm caused by plastic particles at the genetic and cellular levels.

Gene Expression Matrices Large-scale datasets are produced that document which genes become more active (upregulated) or less active (downregulated) when organisms are exposed to microplastics and nanoplastics. These matrices provide a complete picture of how plastic exposure alters the cell’s genetic activity across thousands of genes simultaneously.

Visual Subpopulation Mapping Computational algorithms such as UMAP and t-SNE transform complex genetic data into visual plots that reveal how plastic exposure changes the composition of different cell types within tissues. For example, researchers can observe how exposure increases the proportion of specialized immune cells like CD8⁺ effector T cells, which are involved in fighting infections or damaged cells.

Mutational Signatures Plastic exposure creates distinctive patterns of DNA mutations—such as the conversion of cytosine bases to adenine bases (C>A substitutions)—that serve as a “fingerprint” of exposure to specific plastic contaminants like Bisphenol A (BPA) or styrene oxide. These characteristic mutation patterns help scientists identify which type of plastic damage has occurred and what toxic compounds were responsible.

Pathway Enrichment Analysis Specialized bioinformatics tools like KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analysis identify which fundamental biological processes are being disrupted by plastic exposure. Common disrupted pathways include oxidative phosphorylation (energy production in cells), the MAPK signaling pathway (cellular communication), and chemical carcinogenesis (processes that can lead to cancer).

Microbial Diversity Indices Statistical measures quantify the balance between harmful and beneficial bacteria in the human gut microbiome following plastic ingestion. These indices reveal whether plastic consumption shifts the microbial ecosystem toward pathobionts (disease-promoting microorganisms) or maintains a healthy population of beneficial bacteria.

Base Calling in Next-Generation Sequencing The identification of individual DNA or RNA bases—a process called base calling—represents the foundational analytical step in all next-generation sequencing (NGS) workflows. This process takes place directly within the sequencing instrument (such as those from Illumina, PacBio, or Oxford Nanopore) and converts the biological signals detected by the machine into a readable digital genetic code.

Primary Analysis and Signal Generation On-Platform Processing Base calling occurs as “Primary Analysis,” meaning it happens in real-time while the biological sample is being sequenced inside the machine. The sequencing platform simultaneously reads and identifies nucleotides as the process unfolds.

Technology-Specific Sequencing Methods Different sequencing platforms employ distinct approaches: Short-read sequencing (Illumina): DNA molecules are cut into small fragments ranging from 200 to 500 base pairs long. The sequencing machine then reads these fragments from both ends (paired-end reads), typically generating sequences of approximately 150 base pairs in length, and systematically identifies each nucleotide in order. Long-read sequencing (PacBio and Oxford Nanopore): These technologies sequence complete, unbroken DNA molecules. PacBio’s Single Molecule Real-Time (SMRT) sequencing produces average read lengths of about 20 kilobases, while Oxford Nanopore generates ultra-long reads averaging around 100 kilobases, allowing researchers to capture much larger stretches of genetic information in a single read. Detection of Mutation Patterns The sequencing process identifies specific genetic variations caused by environmental exposures, such as Single Base Substitutions (SBS). These are characteristic mutation patterns at particular locations (such as guanine residues) that indicate damage from toxic chemicals derived from plastics.

Output and File Formats FASTQ Files The direct result of base calling is raw “reads” that are stored in FASTQ format—the standard file format for sequencing data. Data Content Each FASTQ file contains two essential pieces of information for every base identified: the sequence of nucleotides (represented as A, C, G, or T) and an associated quality score, known as a Phred quality score or Q-score, which indicates the confidence level of that base call.

Measuring Accuracy and Quality Control Quality Scoring System To verify that the base calling process is reliable, each identified base is assigned a probability score indicating the likelihood that the identification is correct.

The Standard for High-Quality Data A Q30 score is the widely accepted benchmark for excellent data quality, representing a 99.9% accuracy rate in base identification. This threshold ensures that the sequencing data is trustworthy for downstream analysis.

Quality Filtering During the next phase of analysis, bioinformatics software programs like fastp or FastQC examine the raw data and filter out low-quality reads where base calling may have been uncertain or unreliable, ensuring only high-confidence data moves forward.

Downstream Assembly and Analysis of Decoded Bases After the sequencing machine completes base calling and produces individual reads, the data enters Secondary Analysis, where bioinformatics tools organize and interpret the complete genetic information.

Reference-Based Genome Alignment The decoded genetic sequences are compared against and mapped to a reference genome—such as the human genome (GRCh38) or the pig genome (Sus scrofa)— to determine the correct location of each read within the organism’s full genetic blueprint.

De Novo Assembly When researchers lack a suitable reference genome for comparison, an alternative strategy is employed: the decoded reads are further subdivided into overlapping short segments called k-mers. These k-mers are then reassembled using computational algorithms and graph-based methods to reconstruct longer continuous sequences, known as contigs, that represent the original genetic material.

Also answer the following questions: 1. Is your method first-, second- or third-generation or other? How so? 2. What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps. 3. What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)? 4. What is the output of your chosen sequencing technology?

5.2 DNA Write

(i) What DNA would you want to synthesize (e.g., write) and why? These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :) See some famous examples of DNA design

(ii) What technology or technologies would you use to perform this DNA synthesis and why? Also answer the following questions: 1. What are the essential steps of your chosen sequencing methods? 2. What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

5.3 DNA Edit

(i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why?

(ii) What technology or technologies would you use to perform these DNA edits and why? Also answer the following questions: 1. How does your technology of choice edit DNA? What are the essential steps? 2. What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing? 3. What are the limitations of your editing methods (if any) in terms of efficiency or precision?

CRISPR Base Editing for Precise DNA Modification CRISPR base editing offers a highly precise approach to DNA modification that changes individual bases without breaking both strands of the DNA molecule. This technique represents a significant advancement in genetic engineering because it enables targeted alterations with minimal disruption to the overall genetic structure.

How Base Editing Works The method combines two key components: a specially engineered Cas enzyme and a deaminase protein. The deaminase acts as a molecular converter, transforming one DNA base into another—such as changing cytosine to thymine (C→T) or adenine to guanine (A→G). To target a specific gene like myostatin, researchers design a guide RNA that directs the editing machinery to the correct location. The entire system is then introduced into cells using a plasmid vector and a physical delivery method such as electroporation, which creates temporary pores in the cell membrane to allow the genetic material to enter.

PAM Sequence Requirements and Solutions One constraint of base editing is that the Cas enzyme requires a specific DNA sequence called a PAM (Protospacer Adjacent Motif) located near the target site to function properly. This requirement previously limited the number of editable locations throughout the genome. However, newer Cas9 variants—such as SpRY—can recognize and work with a broader range of PAM sequences, dramatically expanding the number of genomic locations available for targeting and editing.

Improving Precision and Safety Advanced bioinformatics tools like BE-HIVE and Honeycomb have enhanced the effectiveness of base editing by predicting the most promising edit sites and simultaneously reducing the risk of unintended mutations at off-target locations, making the entire process more reliable and safer.

References

1. DNA Sequencing at 40: Past, Present, and Future (2017) Shendure, J., Balasubramanian, S., Church, G. et al. https://doi.org/10.1038/nature24286
2. DNA Synthesis Technologies to Close the Gene Writing Gap (2023), Hoose, A., Vellacott, R., Storch, M. et al. https://doi.org/10.1038/s41570-022-00456-9
3. Recombineering and MAGE (2021), Wannier T, et al. Nat Rev Methods Primers, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9083505/
4. CRISPR Technology: A Decade of Genome Editing is Only the Beginning, Wang, Doudna, et al., https://www.science.org/doi/10.1126/science.add8643
5. GenBank overview: https://www.ncbi.nlm.nih.gov/genbank/
6. NCBI: https://www.ncbi.nlm.nih.gov/genome/
7. Ensembl: https://useast.ensembl.org/index.html
8. UCSC Genome Browser: https://genome.ucsc.edu/
9. Protective and Enhancing Alleles: https://arep.med.harvard.edu/gmc/protect.html
10. Overview of Next Generation Sequencing Technologies - PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC6020069/
11. Sanger Sequencing Steps & Method - MilliporeSigma https://www.sigmaaldrich.com/US/en/technical-documents/protocol/genomics/sequencing/sanger-sequencing
12. DNA Sequencing Technologies, How They Differ, and Why It Matters https://www.fjc.gov/content/361255/dna-sequencing-technologies-how-they-differ-and-why-it-matters
13. sangeranalyseR: Simple and Interactive Processing of Sanger … https://pmc.ncbi.nlm.nih.gov/articles/PMC7939931/
14. NGS Library Preparation - 3 Key Technologies https://www.illumina.com/techniques/sequencing/ngs-library-prep.html
15. Next-Generation Sequencing Technology: Current Trends … - PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC10376292/
16. Overview of PacBio SMRT sequencing: principles, workflow, and … https://www.cd-genomics.com/pacbio-smrt-system-single-molecule-real-time-sequencing.html
17. PacBio sequencing output increased through uniform and … https://www.nature.com/articles/s41598-021-96829-z
18. Library preparation for nanopore sequencing https://oxfordnanoporedx.com/products/prepare
19. Output Structure - Oxford Nanopore Output Specifications https://nanoporetech.github.io/ont-output-specifications/latest/minknow/output_structure/
20. What are the different types of DNA sequencing technologies? https://www.thermofisher.com/us/en/home/life-science/sequencing/sequencing-learning-center/sequencing-basics/dna-sequencing-technologies.html
21. DNA Sequencing Fact Sheet https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Fact-Sheet
22. DNA Sequencing: How to Choose the Right Technology https://frontlinegenomics.com/dna-sequencing-how-to-choose-the-right-technology/
23. Sample Preparation - GENEWIZ from Azenta Life Sciences https://www.genewiz.com/public/resources/sample-submission-guidelines/sanger-sequencing-sample-submission-guidelines/sample-preparation
24. Sanger Sequencing: Introduction, Principle, and Protocol | CD Genomics Blog https://www.cd-genomics.com/blog/sanger-sequencing-introduction-principle-and-protocol/
25. [PDF] Sanger Sequencing Best and Worst Practices - rtsf@msu.edu https://rtsf.natsci.msu.edu/_assets/files/genomics/Sanger_Sequencing_Best_and_Worst_Practices_Guide_25April2024.pdf
26. [PDF] Sanger Sequencing Handbook - FULL SERVICE https://www.biotech.cornell.edu/sites/default/files/2020-08/Full_service_Sanger_Handbook.pdf
27. Sanger Sequencing - Sample Prep & Data Analysis with BLAST https://www.youtube.com/watch?v=ez-_YtHm9pk
28. Videos https://www.neb.com/en/products/next-generation-sequencing-library-preparation/library-preparation-for-illumina
29. The use of LLM to help with finding information and reporting

Week 3 LAB AUTOMATION

Week # 3 Lab Automation

LAB AUTOMATION

To get hands-on (or at least code-on) with pipetting robots.

Your task this week is to Create a Python file to run on an Opentrons liquid handling robot. 0. Review this week’s recitation and this week’s lab for details on the Opentrons and programming it. 1. Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. 2. Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons. ◦ You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept. ◦ If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead.  Ask for help early! 3. If the Python component is proving too problematic even with AI and human assistance, download the full Python script from the GUI website and submit that: Use the download icon pointed to by the red arrow in this diagram. The Python component was problematic and I sent the the python script (1 OTDesign_02-26-26_22-49-52.py)

One of the great parts about having an automated robot is being able to precisely mix, deposit, and run reactions without much intervention, and design and deploy experiments remotely. For this week, we’d like for you to do the following:

1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.

The research papers are referenced below and using cancer research using opentron as described in sections below:

Automating Cancer Research Through Robotic Laboratory Systems Laboratory automation transforms manual pipetting and sample handling into standardized, repeatable robotic processes that enhance throughput and consistency in cancer research. Common platforms such as the Opentrons OT-2 and OT-3 are increasingly deployed to automate large-scale drug screening experiments, three-dimensional organoid cultivation, and protein analysis from clinical samples.

Hardware and Software Architecture Effective automation requires integrating a robotic arm with specialized modules designed to replicate the conditions and functions of a traditional laboratory workbench.

The Robotic Platform The Opentrons OT-2 serves as the central automation unit, featuring a motorized arm that moves along three axes (X, Y, and Z coordinates) and can accommodate up to two electronic pipetting heads—either single-channel or eight-channel configurations—to transfer liquids between containers.

Supporting Hardware Modules Cancer research protocols typically require specialized add-on modules to perform specific tasks: Temperature Modules: These maintain biological reagents and cell culture plates at precise temperatures, such as 4°C for refrigerated storage or 37°C for maintaining cells at body temperature. Magnetic Modules: These devices use magnetic fields to capture and manipulate magnetic beads, which are essential for isolating DNA and RNA or enriching specific proteins from samples. Thermocyclers: Integrated PCR machines mounted directly on the robotic platform allow for on-deck amplification of genetic material during library preparation without removing samples.

Software Control and Customization Researchers can program the Opentrons system using the Python programming language through the Opentrons Python API, which permits conditional instructions and calculations that adjust volumes dynamically. For simpler applications, user-friendly no-code platforms like OT2-CherryPick provide accessible interfaces that require no programming expertise, making the system suitable for straightforward tasks such as transferring samples between plates or mapping sample locations.

Converting Manual Protocols into Automated Workflows Translating a published cancer research method into an automated robotic protocol requires careful deconstruction into standardized components.

Liquid Class Calibration Different biological liquids behave uniquely during pipetting. Viscous solutions like basement membrane extract or volatile substances like ethanol require customized pipetting speeds and discharge volumes to guarantee accuracy and prevent errors.

Deck Mapping and Coordinate Assignment Every location on a 96-well or 384-well plate must be precisely mapped to exact spatial coordinates (x, y, z positions) so the robotic arm can access each well with precision. Converting Manual Steps into Computational Logic Manual instructions such as “perform three washes with phosphate-buffered saline (PBS)” are transformed into Python programming loops that automatically repeat the washing sequence across all plate positions.

Real-World Cancer Research Applications 3D Organoid and Microtissue Development Expanding three-dimensional cell models is essential for capturing the complexity and variation seen in actual tumors. The Scaffold-supported Platform for Organoid-based Tissues (SPOT) uses the Opentrons OT-2 to automate the creation and maintenance of organoids grown from patient tumor samples. This automated method produces results comparable to manual methods while streamlining multiple steps—including tissue generation, adding test drugs, and breaking down the gel matrix for downstream analysis of individual cells. This integration reduces labor and improves consistency.

High-Throughput Protein Analysis in Cancer Immunotherapy Identifying disease-associated proteins in blood plasma from cancer patients requires processing large numbers of samples rapidly. Automated workflows on the OT-2 have successfully streamlined the entire analysis pipeline—from preparing samples through to loading them onto specialized mass spectrometry instruments—enabling analysis of up to 192 patient samples within a 6-hour window. This capability was applied to examine how immune checkpoint inhibitors alter the protein composition of blood plasma in patients with advanced melanoma.

Automated Management of Cancer Cell Lines Maintaining living cancer cells in culture—whether they grow attached to surfaces or suspended in liquid—presents a significant operational challenge. The Automated Cell Culture Splitter (ACCS), developed using the Opentrons OT-2, incorporates an integrated imaging system that counts living cells in real-time, allowing the robot to automatically seed new plates at precisely controlled cell densities. This approach reduces hands-on labor by more than 61% while achieving remarkably consistent seeding across wells, with variation remaining below 11%.

Testing and Quality Assurance Before Running Experiments Before executing a protocol with valuable or limited patient samples, researchers must validate the automated workflow through multiple verification steps: Virtual Simulation: Software tools like opentrons_simulate perform a computer-based “dry run” of the protocol to identify potential coordinate errors or physical collisions between the robotic arm and laboratory equipment before the robot actually moves. Water Runs: The complete protocol is executed using water colored with dyes, allowing researchers to visually confirm that the correct volumes are being transferred and that solutions are mixing properly throughout the process. Real-Time Observation: Imaging modules integrated into the system monitor the status of cells or organoids during automated runs, ensuring that cultures are progressing normally and providing immediate feedback if adjustments are needed.

References

1. Avci, M. B. (2026). An integrated platform for liquid handling and cell imaging in life science applications. PMC.

2. Cao, R., Li, N. T., Latour, S., Cadavid, J. L., Tan, C. M., Forman, A., Jackson, H. W., & McGuigan, A. P. (2023). An automation workflow for high‐throughput manufacturing and analysis of scaffold‐supported 3D tissue arrays. Advanced Healthcare Materials, 12. https://doi.org/10.1002/adhm.202202422

3. Courville, G., Vaid, S., Toruño, A., Lebel, P., Cabrera, J. P., Raghavan, P., Jacobsen, A., Bell, G., Leonetti, M. D., & Gómez-Sjöberg, R. (2024). Open-source cell culture automation system with integrated cell counting for passaging microplate cultures. PNAS Nexus. https://doi.org/10.1101/2024.12.27.629034

4. Fusco, R. (2026). OT2-CherryPick: A zero-install web platform for orchestrating complex liquid handling on the Opentrons OT-2. ChemRxiv. https://doi.org/10.26434/chemrxiv.15000637

5. Kverneland, A. H., Harking, F., Vej-Nielsen, J. M., Huusfeldt, M., Bekker-Jensen, D. B., Svane, I. M., Bache, N., & Olsen, J. V. (2023). Fully automated workflow for integrated sample digestion and Evotip loading enabling high-throughput clinical proteomics. PubMed. https://doi.org/10.1101/2023.12.22.573056

   2. Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts,

    3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details.
   While your description/project idea doesn’t need to be set in stone, we would like to see core details of what you would automate. This is due at
   the start of lecture and does not need to be tested on the Opentrons yet.
   

Automating Pancreatic Cancer Research with Opentrons Converting a manual cancer assay into an automated liquid-handling system involves standardizing sample preparation, distributing reagents, mixing samples, managing incubation periods, and preparing materials for final analysis. The Opentrons platform facilitates this transformation by offering a collection of pre-built protocols, user-friendly workflow design tools without requiring programming, and a Python-based application programming interface for custom development. The system is specifically designed to support the types of experiments common in cancer research, including genomic analysis, cell biology studies, and assays using cultured cells.

Essential Components for Automation Successfully automating a pancreatic cancer research workflow depends on establishing a clear scientific objective, breaking the experimental procedure into discrete robotic steps, and assigning appropriate equipment to each stage.

Starting with a Clear Research Goal The first step is identifying the specific biological question driving the research. Pancreatic cancer investigations typically focus on one of three main approaches: analyzing individual cells to understand their molecular characteristics, testing patient-derived tumor models to see how they respond to drugs, or preparing genetic material for next-generation sequencing analysis. The Opentrons system excels in situations where you need to perform the same pipetting task reliably across numerous wells, multiple patient samples, or many different experimental conditions.

Converting the Assay into Discrete Robotic Operations Breaking down the experiment into individual automation steps is essential. A typical automated pancreatic cancer assay includes setting up plates, equalizing reagent concentrations, moving cells or tissue samples between containers, creating series of decreasing concentrations for dose-response studies, breaking open cells or preserving cell structures, isolating target molecules using magnetic bead separation, amplifying DNA segments, and constructing libraries for sequencing. The Opentrons protocol library contains established workflows for nucleic acid isolation, sequencing library preparation, protein detection assays (ELISA), and cell-based experiments—all fundamental building blocks for pancreatic cancer research.

Connecting Assay Steps to Hardware Resources Each step of the assay must be matched to the appropriate robotic tools and accessories. Specify which liquid-dispensing devices (pipettes), storage containers for pipette tips, temperature-control modules, or magnetic separation tools the protocol requires, then input the exact locations and volumes within the robotic workspace. Both the Flex platform and Opentrons’ Python API support automation ranging from straightforward liquid transfers to highly customized workflows, including connection to external instruments or software systems.

Research Applications for Pancreatic Cancer Single-Cell Analysis and Sequencing Library Preparation The most promising automation opportunity for pancreatic cancer research centers on single-cell multiomics—techniques that reveal multiple molecular characteristics (genomics, proteomics, etc.) from individual cells—and automating the capture and library-preparation steps required for sequencing. This focus is particularly valuable because understanding tumor heterogeneity (the differences between cancer cells within a single tumor) and moving discoveries from research to clinical practice both depend on these methods. A notable example is the partnership between BD and Opentrons to automate cell isolation and sequencing library construction on the Opentrons Flex platform, targeting both fundamental disease research and pharmaceutical development.

Three-Dimensional Tumor Models and Drug Testing Another significant application is automating the creation and screening of 3D tumor models—including spheroids grown under conditions mimicking the oxygen-poor, fibrotic tumor microenvironment. Published research on pancreatic cancer has demonstrated that high-throughput automation of spheroid platforms improves the reliability and scalability of tumor biology studies. Although these studies may not exclusively use Opentrons, the same core automation principles apply: standardized dispensing of liquids, precisely timed incubations, and controlled sample movement all reduce experimental inconsistency and strengthen the robustness of drug screening results.

Building a Practical Automated Workflow A realistic Opentrons-based workflow for pancreatic cancer research follows this sequence:

1. Sample intake and standardization: Receive and prepare patient samples so they are comparable across the experiment.
2. Automated reagent distribution: Dispense solutions into 96- or 384-well plates with precision.
3. Cell or tissue model setup: Seed cells or organize 3D spheroids if modeling tumor structure.
4. Controlled waiting periods: Allow reactions to proceed off the robot or using integrated heating modules.
5. Magnetic bead-based purification: Isolate target molecules using magnetic separation.
6. Genetic amplification or sequencing preparation: Set up DNA amplification or library construction for sequencing.
7. Sample finalization and export: Seal plates and prepare them for downstream analysis. This structured approach is particularly valuable for pancreatic cancer research because patient samples are frequently scarce and genetically diverse, so automation conserves sample material, eliminates human pipetting errors, and ensures that replicate samples are processed identically.

Implementing Automation Choosing a Starting Point Begin by checking whether the Opentrons protocol library contains a workflow similar to the assay, because using an existing protocol is the fastest way to access a validated and tested starting point. For custom or modified assays, a Python protocol that specifies the containers, pipettes, liquid properties, transfer volumes, mixing instructions, and module operations, then test it thoroughly with small-scale trials before expanding to larger experiments.

Development Strategy A proven approach to establishing a reliable automated assay follows these stages:

1. Start with a single plate and one type of sample to confirm basic functionality.
2. Test how much liquid remains unusable, verify transfer precision, and confirm timing is appropriate.
3. Incorporate positive and negative control samples and replicate wells.
4. Confirm that the automated method produces biological results identical to hand-performed pipetting.
5. Only after verifying the assay’s stability should you increase the scale to more samples or plates.

Why Opentrons Works for Pancreatic Cancer Research The Opentrons system is particularly well-suited for pancreatic cancer workflows that require many repetitive liquid-handling steps—such as single-cell genetic analysis, sample preparation, protein measurement assays, or testing how patient tumor organoids respond to drug treatments. The advantage extends beyond simply completing work faster; consistency across wells and between experimental runs is equally important, especially in pancreatic cancer research where tumor microenvironment variations and limited patient material mean that experimental errors are costly and difficult to replicate. A practical example illustrates this value: an automated pipeline could receive patient-derived pancreatic cancer cells, apply chemicals to dissociate them into single cells, adjust cell numbers so they are consistent across samples, distribute cells into test wells, set up a series of drug concentrations to evaluate treatment responses, and prepare sequencing libraries from surviving cells. This type of integrated workflow aligns with Opentrons’ flexible automation framework and reflects the emerging focus on fully automated, multiomics analysis in cancer research.

References

1. Opentrons Unveils New Protocol Library and Generative AI Tools to … https://opentrons.com/archives/news/opentrons-unveils-new-protocol-library-and-generative-ai-tools-to-accelerate-lab-automation-and-scale-scientific-research
2. Protocol Library - Opentrons https://opentrons.com/intro-to-protocol-library
3. BD, Opentrons Partner to Automate Single-Cell Multiomics Workflows https://clpmag.com/lab-essentials/lab-automation/bd-opentrons-partner-automate-single-cell-multiomics-workflows/
4. AI in lab automation: Opentrons Flex boosts experimental protocols https://www.drugdiscoverytrends.com/ai-in-lab-automation-opentrons-flex/
5. BD and Opentrons Collaborate to Accelerate Single-Cell Multiomics Discoveries with Robotic Automation https://www.morningstar.com/news/pr-newswire/20251008ny92700/bd-and-opentrons-collaborate-to-accelerate-single-cell-multiomics-discoveries-with-robotic-automation
6. Evaluating the biological effectiveness of boron neutron capture … https://pubs.rsc.org/en/content/articlelanding/2023/an/d2an01812h
7. Opentrons unveils new protocol library and generative AI tools https://www.selectscience.net/article/opentrons-unveils-new-protocol-library-and-generative-ai-tools
8. Using Patient-Derived Organoids to Predict Treatment Response in … https://www.genengnews.com/topics/cancer/using-patient-derived-organoids-to-predict-treatment-response-in-pancreatic-cancer/
9. BD and Opentrons collaborate to accelerate single-cell multiomics … https://www.news-medical.net/news/20251009/BD-and-Opentrons-collaborate-to-accelerate-single-cell-multiomics-discoveries-with-robotic-automation.aspx
10. BD and Opentrons Collaborate to Accelerate Single-Cell Multiomics … https://news.bd.com/2025-10-08-BD-and-Opentrons-Collaborate-to-Accelerate-Single-Cell-Multiomics-Discoveries-with-Robotic-Automation
11. Biological Approaches to Therapy of Pancreatic Cancer - PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC2882228/
12. Accelerating Drug Discovery with Automation and A.I. with UTMB https://opentrons.com/utmb
13. FDA Approves First-of-Its-Kind Device to Treat Pancreatic Cancer https://www.fda.gov/news-events/press-announcements/fda-approves-first-its-kind-device-treat-pancreatic-cancer
14. What Does Opentrons Do? | Directory - PromptLoop https://www.promptloop.com/directory/what-does-opentrons-do
15. An Automation Workflow for High‐Throughput Manufacturing … - PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC11468893/
16. Detection of pancreatic cancer using serum protein profiling https://www.sciencedirect.com/science/article/pii/S1365182X15314453
17. Proton Therapy in the Management of Pancreatic Cancer - PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC9179382/
18. Artificial intelligence in pancreatic cancer - PMC - NIH https://pmc.ncbi.nlm.nih.gov/articles/PMC9576619/
19. CAP Cancer Protocol Pancreas Exocrine https://documents.cap.org/protocols/cp-pancreas-exocrine-17protocol-4001.pdf
20. The use of LLM to help with finding information and reporting

Week 4: PROTEIN DESIGN PART I

Week # 4 Protein Design Part I

PROTEIN DESIGN PART I

To look at how sequence, structure, and energetics can be modeled and manipulated to create or optimize proteins with specified functions.

Answer any NINE of the following questions from Shuguang Zhang: (i.e. you can select two to skip)

• 1. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)
     For a Tilapia Fish: Assuming : meat = 20% protein by weight; average amino acid ≈ 100 Da (g/mol). Calculation: • Protein mass = 500 g × 0.20 = 100 g • Moles of amino-acid residues = 100 g ÷ 100 g·mol⁻¹ = 1.00 mol • Number of amino-acid molecules  using Avogadro’s number ≈ 1.00 × ≈ 6.02 × 10^{23 = 6.02 × 10}23 amino-acid molecules.
  • 2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?

The beef meat is in the form of amino acids that our body needs which is broken down by the enzymes in our stomach to the amino acids required by our body. The amino acids are the building blocks of DNA. Beef also provides protein, zinc and several D vitamins used for muscle health, iron that boosts our immune system

• 3. Why are there only 20 natural amino acids? Through evolutionary selection there are 20 natural amino acids that are encoded by the genetic code used by all life forms for protein synthesis, although there are more than 500 amino acids that exist in nature.

4. Can you make other non-natural amino acids? Design some new amino acids. There are scientists who have designed new amino acids like the Cyclopropyl‑alanine (cPrAla) whose properties are small, rigid , steric probe that increases backbone constraint. Its uses are conformational stabilization and protease resistance.

5. Where did amino acids come from before enzymes that make them, and before life started? Amino acids could have been synthesized through multiple non-biological chemical pathways on the early Earth. Electric discharges and ultraviolet radiation acting on simple atmospheric gases generated diverse mixtures of organic compounds, including compounds like alanine and glycine. For these organic building blocks to combine into short peptide chains, the process likely required special activation mechanisms—such as energized compounds like thioesters or cyanamide—along with repeated cycles of drying and rewetting, or assistance from mineral surfaces acting as catalysts. Importantly, enzymes like those found in modern life were not yet available to facilitate these reactions. Evidence suggests that the raw materials for life may have arrived from space itself: meteorites containing carbon-based compounds (such as the Murchison meteorite) and material from comets both carry amino acids and organic molecules, demonstrating that similar chemical reactions can occur naturally beyond Earth and could have supplied the ingredients for the early planet. Additional experimental pathways support this scenario. Ultraviolet light shining on frozen water or heating formamide (a simple organic compound) produces amino acids and related molecules under conditions that are colder or more oxidizing than those used in earlier laboratory experiments. Finally, geothermal hot vents and rocky mineral surfaces—particularly those containing metal sulfides and clay minerals—would have naturally concentrated these chemical precursors and promoted their assembly into peptide chains.

6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect? It would be the Left‑handed — D‑amino acids form the mirror‑image (left‑handed) α‑helix corresponding to the right‑handed helix made from L‑amino acids (same geometry mirrored). It is the one preferred for drug design as its more resistant to breakdown.

7. Can you discover additional helices in proteins? It is possible by the use of a deep learning system such as Alpha Fold to predict a protein’s 3D atomic structure from its amino acid sequence.

8. Why are most molecular helices right-handed? Proteins naturally favor a right-handed helical structure due to the molecular properties of the L-amino acids from which they are built. The three-dimensional arrangement of atoms in L-amino acids—their stereochemical configuration and the steric (spatial) and electronic (charge-based) forces acting on them—determines which backbone torsion angles (φ, ψ) are energetically favorable. These preferred angles naturally align with a right-handed helical geometry, which represents the lowest-energy, most stable configuration. This structural preference is further reinforced by the D-sugars present in the DNA and RNA backbone. The presence of D-sugars (rather than their mirror-image L-form) in nucleic acids creates a similar geometric environment that also favors and supports the right-handed helical arrangement, making it consistent with the overall architecture of proteins built from L-amino acids. In essence, the molecular “handedness” of biological building blocks—L-amino acids and D-sugars—creates a coordinated system where all the natural forces involved push the same direction, promoting right-handed helical structures as the thermodynamically favored state.

9. Why do β-sheets tend to aggregate?

Aromatic interactions: The π–π stacking of aromatic amino acids (phenylalanine, tyrosine, and tryptophan) creates additional stabilization through their interactions, further reinforcing stacked sheet structures. Nucleation and templating: Once an initial molecular nucleus forms, subsequent strands can attach to this template, which lowers the energy barrier for further assembly and accelerates the growth of the fibril structure—a process driven by kinetic facilitation. Side-chain packing and van der Waals forces: The complementary, regularly repeating interfaces between side chains (sometimes called “steric zippers”) allow for maximal close-contact packing between molecules, which maximizes the weak dispersion forces (van der Waals interactions) that contribute to overall stability. Backbone hydrogen bonding: Extended networks of hydrogen bonds form between the backbone atoms of adjacent strands (specifically between N–H groups and C=O groups across different molecules), which significantly stabilize the β-sheet structure between strands. Dehydration and entropic effects: Thermodynamic stability increases when water molecules are expelled from the interfaces between molecules and when the total surface area exposed to solvent decreases. Hydrophobic effect: When nonpolar (hydrophobic) side chains are buried inside the assembled structure as sheets stack together, ordered water molecules are released into solution. This release of water increases the system’s entropy, providing a large favorable thermodynamic driving force. Polar side-chain networks: Amino acids with polar side chains (such as glutamine and asparagine) form interconnected hydrogen-bonding patterns that act cooperatively to stabilize and strengthen the fibril structure.

10. Why do many amyloid diseases form β-sheets?

β-sheets are the thermodynamically favored conformation for proteins involved in amyloid diseases due to the multiple stabilizing factors that work together. The backbone hydrogen bonding between extended strands creates a very stable intermolecular network. Additionally, the hydrophobic effect is particularly powerful—when nonpolar amino acid side chains are buried within stacked β-sheets, water molecules are released, which provides a large entropic gain that drives assembly. Kinetic factors further promote β-sheet formation. Once a small nucleus of β-sheet structure forms, it acts as a template. New protein molecules can rapidly attach to this growing template with a lower activation energy than forming the initial nucleus, creating a positive feedback loop that accelerates fibril growth. The β-sheet structure is also highly resistant to degradation. The extensive hydrogen-bonding networks and tightly packed interfaces make these aggregates very difficult for cellular machinery to break down or clear, allowing amyloid fibrils to accumulate over time. Additionally, certain proteins are inherently prone to adopting β-sheet conformations due to their amino acid sequences and three-dimensional properties. In amyloid diseases, proteins that normally fold into other structures (like α-helices) become misfolded under stress conditions and instead spontaneously assemble into the more stable β-sheet configuration. In essence, the combination of thermodynamic stability, kinetic acceleration through templating, and cellular difficulty in clearing these structures makes β-sheets the inevitable outcome for proteins that misfold in amyloid diseases.

• ◦ Can you use amyloid β-sheets as materials? Amyloid β-sheets are gaining recognition as valuable materials for practical applications across multiple industries, leveraging their distinctive structural and functional characteristics.

Healthcare Applications Amyloid β-sheets can be modified and designed to serve as delivery vehicles for pharmaceuticals, enabling them to direct treatments to particular cell types or anatomical locations. Their exceptional mechanical robustness and structural durability make them ideal candidates for use as supportive frameworks in regenerative medicine, where they can facilitate the growth and organization of new tissue.

Sensing and Detection The self-assembling capability of amyloid β-sheets offers promise for creating highly sensitive detection systems. These structures can be incorporated into biosensing devices that identify and measure biological molecules or contaminants present in the environment with enhanced precision.

Environmental and Sustainability Applications As there is growing interest in environmentally responsible material alternatives, amyloid β-sheets represent a promising option for developing compostable or biodegradable materials that could replace conventional petroleum-based plastics. This application addresses the need for sustainable solutions to reduce plastic waste and environmental contamination. In summary, the stability, mechanical properties, and programmable assembly of amyloid β-sheets position them as versatile engineering materials that can address challenges in medicine, environmental monitoring, and sustainable manufacturing.

11. Design a β-sheet motif that forms a well-ordered structure. The foundation of creating a well-ordered β-sheet involves establishing an alternating pattern of water-repelling and water-attracting amino acids along the protein backbone. By arranging hydrophobic (nonpolar) residues to point inward and hydrophilic (polar) residues to point outward, you create a striped arrangement where the nonpolar side chains cluster together in the interior of the sheet while polar groups remain accessible to the aqueous surroundings. This pattern naturally promotes the formation of stable, regular β-sheet structures. To further enhance stability, aromatic amino acids should be distributed at regular intervals throughout the sequence. These aromatic residues (such as phenylalanine and tyrosine) interact through π–π stacking, which reinforces the stacking of sheets on top of each other and creates additional geometric constraints that lock the structure into place.

A Practical Example A straightforward and effective sequence would be: [I-V-F-L-Y-L-F-V-I-V-F-L-Y-L-F-V], composed primarily of isoleucine, valine, leucine, phenylalanine, and tyrosine. These residues are small and hydrophobic, allowing them to pack tightly together while aromatic residues facilitate lateral interactions. For applications requiring additional functionality, incorporating charged amino acids like glutamate at regular intervals adds surface solubility a nd potential binding sites without disrupting the core sheet geometry. A modified version might be: [I-E-F-L-Y-L-F-E-I-E-F-L-Y-L-F-E].

Why This Design Works Hydrophobic side-chain burial drives rapid self-assembly because water molecules trapped within the nonpolar interior are released as the structure forms, providing a large entropy gain. The repeating, predictable pattern ensures that once initial nucleation occurs, subsequent protein molecules attach to the growing structure in an orderly fashion, promoting uniform, large-scale assembly. The complementary packing of side chains maximizes weak intermolecular forces, while the extensive hydrogen-bonding networks between backbones of adjacent strands create remarkable stability.

Customization Options To strengthen the material further, you can increase the proportion of aromatic and β-branched residues to achieve denser packing. For tailored applications, replace specific residues with histidine or aspartate to create binding sites for metal ions, or introduce serine or threonine for potential chemical modifications. To control the final size of assembled structures, strategically insert proline residues to act as “breaks” that limit sheet expansion and create defined structural units. This rational design approach—carefully balancing hydrophobic and hydrophilic properties while incorporating multiple stabilizing interactions—represents the practical framework for engineering functional amyloid-based materials.

Part B: Protein Analysis and Visualization

In this part of the homework, you will be using online resources and 3D visualization software to answer questions about proteins. Pick any protein (from any organism) of your interest that has a 3D structure and answer the following questions:

1. Briefly describe the protein you selected and why you selected it. PD-1 (programmed cell death protein 1) was selected as the subject of study. This protein is an inhibitory receptor located on the cell membrane of T cells and other immune cells. When PD-1 binds to its ligands—PD-L1 or PD-L2—the binding signal suppresses the activation and proliferation of T cells, essentially acting as a “brake” on immune responses. This checkpoint mechanism normally serves an important regulatory function, but tumors and certain pathogens have learned to exploit it. By producing and displaying PD-L1 or PD-L2 on their surfaces, cancer cells and infectious agents can hijack this natural control system. They force T cells to express PD-1 and engage it with the pathogen’s or tumor’s PD-ligands, effectively disabling the immune response that would otherwise attack them. However, blocking the interaction between PD-1 and its ligands can reverse this immune suppression. When the PD-1/PD-L1 (or PD-L2) connection is prevented, T cells regain their ability to recognize and attack cancer cells or infected cells. This principle forms the basis of checkpoint inhibitor immunotherapies, which have proven highly effective in treating certain cancers and enhancing immune responses against persistent infections.

2. Identify the amino acid sequence of your protein.

MQIPQAPWPVVWAVLQLGWRPGWFLDSPDRPWNPPTFSPALLVVTEGDNATFTCSFSNTSESFVLNWYRMSPSNQTDKLAAFPEDRSQPGQDCRFRVTQLPNGRDFHMSVVRARRNDSGTYLCGAISLAPKAQIKESLRAELRVTERRAEVPT AHPSPSPRPAGQFQTLVVGVVGGLLGSLVLLVWVLAVICSRAARGTIGARRTGQPLKEDPSAVPVFSVDYGELDFQWREKTPEPPVPCVPEQTEYATIVFPSGMGTSSPARRGSADGPRSAQPLRPEDGHCSWPL

    ◦ How long is it? What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids.

https://colab.research.google.com/drive/1vlAU_Y84lb04e4Nnaf1axU8nQA6_QBP1?authuser=2#scrollTo=P8i5q0zEoi4J

PD-1 has a length of 288, analyzing using the notebook P appears to be the most frequent at 33.

    ◦ How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs.

I used the pBLAST tool to search for homologs and ClustalOmega to align and visualize them.

After visualizing there are 100 homologs.

    ◦ Does your protein belong to any protein family?

Yes. It is a member of the of the immunoglobulin superfamily and of the CD28/CTLA‑4 family of T‑cell co‑stimulatory/inhibitory receptors.

3. Identify the structure page of your protein in RCSB https://www.rcsb.org/structure/6UMU

    ◦ When was the structure solved? Is it a good quality structure? Good quality structure is the one with good resolution. Smaller the better 
    (Resolution: 2.70 Å)

It was resolved and Deposited : 2019-10-10 and Released: 2019-11-27 to the public. The Resolution: 1.18 Å of high quality and precise as it is below 2.70 Å

    ◦ Are there any other molecules in the solved structure apart from protein?

Yes, Chloride Ion and Water. ◦ Does your protein belong to any structure classification family? Yes,the extracellular domain of PD‑1 has an immunoglobulin V‑set (IgV) fold and is classified with Ig‑like domains in structural databases (SCOP/CATH/Pfam). It is placed in the Ig‑superfamily structural class (consistent with its membership in the CD28/CTLA‑4 receptor family). In the structure classification family database it is SCOP ID: 8059476 and SCOP ID: 8059477

4. Open the structure of your protein in any 3D molecule visualization software: ◦ PyMol Tutorial Here (hint: ChatGPT is good at PyMol commands) ◦ Visualize the protein as “cartoon”, “ribbon” and “ball and stick”. ◦ Color the protein by secondary structure. Does it have more helices or sheets? It has no alpha helices and 2 beta sheets. Adopts an immunoglobulin domain fold with a two-layer beta sandwich architecture. ◦ Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?

It has a hydrophobic core to support the beta sandwich architecture and the hydrophilic residues on the surface
◦ Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)? Yes, as seen above.

Part C. Using ML-Based Protein Design Tools

In this section, we will learn about the capabilities of modern protein AI models and test some of them in your chosen protein.

1. Copy the HTGAA_ProteinDesign2026.ipynb notebook and set up a colab instance with GPU.
2. Choose your favorite protein from the PDB.
3. We will now try multiple things in the three sections below; report each of these results in your homework writeup on your HTGAA website:

C1. Protein Language Modeling

1. Deep Mutational Scans a. Use ESM2 to generate an unsupervised deep mutational scan of your protein based on language model likelihoods.

I went through the steps in the jupiter notebook as shown in the recitation and created the mutation scan heat map below for protein PD-1. There were errors encountered as I run the code for each section,but Gemini was able to rectify the problem and run without problems producing the image below.

    b. Can you explain any particular pattern? (choose a residue and a mutation that stands out) 

Log-likelihood ratio (LLR) used in mutation analysis to help quantify the impact of mutations on protein function by comparing the likelihood of mutant versus wild-type residues. Using Gemini, the Most Favorable Mutation will be the one with the highest LLR where a change from Valine (V) to Leucine (L) at position 187, with an LLR of 5.4932. This suggests that substituting Valine with Leucine at this position could potentially be beneficial.. The Least Favorable Mutation with lowest LLR is a change from Methionine (M) to Isoleucine (I) at position 1, with an LLR of -16.6267. This indicates that this particular substitution is highly unfavorable.

    c. (Bonus) Find sequences for which we have experimental scans, and compare the prediction of the language model to experiment.
2. Latent Space Analysis
    a. Use the provided sequence dataset to embed proteins in reduced dimensionality.
    b. Analyze the different formed neighborhoods: do they approximate similar proteins?
    c. Place your protein in the resulting map and explain its position and similarity to its neighbors.

C2. Protein Folding

Folding a protein 1. Fold your protein with ESMFold. Do the predicted coordinates match your original structure? 2. Try changing the sequence, first try some mutations, then large segments. Is your protein structure resilient to mutations? C3. Protein Generation Inverse-Folding a protein: Let’s now use the backbone of your chosen PDB to propose sequence candidates via ProteinMPNN 1. Analyze the predicted sequence probabilities and compare the predicted sequence vs the original one. 2. Input this sequence into ESMFold and compare the predicted structure to your original.

References

1. The use of LLM to help with finding information and reporting

Week 5: PROTEIN DESIGN PART II

Week # 5 Protein Design Part II

PROTEIN DESIGN PART II

To learn how cutting-edge AI and protein language models are used to design functional proteins and peptides “in silico”.

Part A: SOD1 Binder Peptide Design (From Pranam)

Superoxide dismutase 1 (SOD1) is a cytosolic antioxidant enzyme that converts superoxide radicals into hydrogen peroxide and oxygen. In its native state, it forms a stable homodimer and binds copper and zinc. Mutations in SOD1 cause familial Amyotrophic Lateral Sclerosis (ALS). Among them, the A4V mutation (Alanine → Valine at residue 4) leads to one of the most aggressive forms of the disease. The mutation subtly destabilizes the N-terminus, perturbs folding energetics, and promotes toxic aggregation. Your challenge: 1. Design short peptides that bind mutant SOD1. 2. Then decide which ones are worth advancing toward therapy. You will use three models developed in our lab: • PepMLM: target sequence-conditioned peptide generation via masked language modeling • PeptiVerse: therapeutic property prediction • moPPIt: motif-specific multi-objective peptide design using Multi-Objective Guided Discrete Flow Matching (MOG-DFM)

Part 1: Generate Binders with PepMLM

1. Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation.

2. Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card:
3. Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence.

4. To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison.
5. Record the perplexity scores that indicate PepMLM’s confidence in the binders.

Part 2: Evaluate Binders with AlphaFold3

1. Navigate to the AlphaFold Server: alphafoldserver.com

2. For each peptide, submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein-peptide complex.

3. Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried?

4. In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated peptide matches or exceeds the known binder.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

Structural confidence alone is insufficient for therapeutic development. Using PeptiVerse, let’s evaluate the therapeutic properties of your peptide! For each PepMLM-generated peptide:

1. Paste the peptide sequence.
2. Paste the A4V mutant SOD1 sequence in the target field.
3. Check the boxes
   1. Predicted binding affinity
   2. Solubility
   3. Hemolysis probability
   4. Net charge (pH 7)
   5. Molecular weight Compare these predictions to what you observed structurally with AlphaFold3. In a short paragraph, describe what you see. Do peptides with higher ipTM also show stronger predicted affinity? Are any strong binders predicted to be hemolytic or poorly soluble? Which peptide best balances predicted binding and therapeutic properties? Choose one peptide you would advance and justify your decision briefly.

Part 4: Generate Optimized Peptides with moPPIt

Now, move from sampling to controlled design. moPPIt uses Multi-Objective Guided Discrete Flow Matching (MOG-DFM) to steer peptide generation toward specific residues and optimize binding and therapeutic properties simultaneously. Unlike PepMLM, which samples plausible binders conditioned on just the target sequence, moPPIt lets you choose where you want to bind and optimize multiple objectives at once.

1. Open the moPPit Colab linked from the HuggingFace moPPIt model card
2. Make a copy and switch to a GPU runtime.
3. In the notebook:
   1. Paste your A4V mutant SOD1 sequence.
   2. Choose specific residue indices on SOD1 that you want your peptide to bind (for example, residues near position 4, the dimer interface, or another surface patch).
   3. Set peptide length to 12 amino acids.
   4. Enable motif and affinity guidance (and solubility/hemolysis guidance if available). Generate peptides.
4. After generation, briefly describe how these moPPit peptides differ from your PepMLM peptides. How would you evaluate these peptides before advancing them to clinical studies?

References

1. The use of LLM to help with finding information and reporting

Week 6: GENETIC CIRCUITS PART I

Week # 6 Genetic Circuits Part I

GENETIC CIRCUITS PART I

To learn core molecular biology tools and techniques for processing and assembling DNA, including PCR and Gibson Assembly.

Assignment: DNA Assembly

Answer these questions about the protocol in this week’s lab:

1. What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?

Phusion High-Fidelity PCR Master Mix is a comprehensive formulation that supplies all the essential components required for precise and efficient DNA amplification through the polymerase chain reaction (PCR). The mixture contains Phusion polymerase, an enzyme renowned for its exceptional accuracy in synthesizing new DNA strands during the amplification process. It also includes deoxynucleotide triphosphates (dNTPs), which serve as the molecular building blocks that polymerase incorporates into the growing DNA chains. Additionally, magnesium chloride (MgCl₂) is present as a critical cofactor—an enabling molecule that the polymerase enzyme requires to function optimally and catalyze the formation of new DNA bonds. Finally, the formulation includes a reaction buffer solution that maintains the proper chemical environment throughout the PCR process. This buffer preserves stable pH levels and regulates salt concentration, ensuring that all enzymatic reactions proceed smoothly and that the overall amplification process achieves maximum efficiency. In essence, Phusion High-Fidelity PCR Master Mix eliminates the need to manually combine individual components—it is a ready-to-use formulation where all necessary ingredients are already optimized and proportioned for reliable, high-fidelity DNA amplification.

2. What are some factors that determine primer annealing temperature during PCR?

The temperature at which primers attach to target DNA during PCR is determined by several interconnected factors that collectively influence how effectively and specifically the primers bind to their complementary DNA sequences. The melting temperature (Tm) of the primers is a central parameter that determines this annealing temperature. Melting temperature represents the specific thermal point at which approximately half of all DNA-primer complexes denature and separate from each other. This value is not fixed—it varies depending on the chemical composition of the primer sequence itself. A major factor affecting Tm is the GC content of the primer. Since guanine-cytosine base pairs form stronger hydrogen bonds compared to adenine-thymine base pairs, primers with a higher percentage of G and C nucleotides exhibit higher melting temperatures. Conversely, primers rich in adenine and thymine have lower Tm values. This difference in bond strength directly correlates with thermal stability. Beyond the primer sequence itself, the salt concentration within the PCR reaction buffer also significantly influences binding stability. Salts present in the solution help reinforce and stabilize the interaction between primers and their target DNA by shielding the negative charges on the DNA backbone, reducing electrostatic repulsion and promoting tighter primer-template binding. In essence, optimizing the primer annealing temperature requires balancing the intrinsic properties of the primer sequence (particularly its GC content and resulting Tm) with the chemical conditions of the reaction environment (salt concentration).

3. There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other.

Two Complementary Approaches to DNA Fragmentation Two distinct strategies can be employed to generate linear DNA fragments: polymerase chain reaction (PCR) amplification and restriction enzyme digestion. Each method operates on different principles and offers distinct advantages depending on the experimental objective.

PCR Method PCR is a DNA amplification technique that simultaneously increases the quantity of DNA while allowing for deliberate sequence modifications. During amplification, this method can introduce desired changes into the DNA—such as specific mutations or sequence overlaps that facilitate subsequent cloning steps. A key characteristic of PCR is that it relies on primer binding to guide amplification rather than targeting specific nucleotide sequences for cutting. This means the method is not constrained by the presence or absence of predefined restriction sites.

Restriction Enzyme Digestion In contrast, restriction enzyme digestion employs specialized proteins that function as molecular scissors. These enzymes recognize and cut DNA exclusively at specific sequence motifs that serve as their recognition sites. However, this method has a critical limitation: it can only be used successfully if the target DNA contains those specific recognition sequences at the desired locations. Choosing the Appropriate Method PCR is the preferred approach when your goal is to alter or engineer the DNA sequence or when the precise locations where you want to separate the DNA are unknown or unavailable as restriction sites. Restriction digestion becomes the better choice when you have already identified the exact locations where cuts should be made and the DNA contains appropriate restriction sites at those positions.

4. How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?

Preparing DNA Fragments for Gibson Assembly To successfully prepare DNA fragments for Gibson cloning, several critical requirements must be met to ensure seamless assembly of the final construct.

Overlapping Sequence Regions Each DNA fragment must contain complementary overlapping sequences at its ends, typically ranging from 20 to 40 base pairs in length. These overlapping regions are essential for the assembly process—they allow the individual DNA pieces to recognize and bind to each other, providing the molecular “glue” that holds the fragments together during Gibson cloning.

Absence of Interfering Restriction Sites It is crucial to verify that the DNA fragments do not contain internal restriction enzyme recognition sites that could be problematic. If such sites are present within the fragments themselves, they could lead to unwanted cutting or interference with the joining process, compromising the success of the cloning procedure.

Proper Fragment Orientation The fragments must be arranged and oriented in the correct order and direction relative to one another. This proper orientation ensures that when the pieces assemble, they form a functional gene or plasmid with the correct genetic sequence and regulatory elements. Incorrect orientation would result in non-functional or defective constructs. In summary, successful Gibson cloning depends on careful design of overlapping sequences, verification of fragment integrity, and precise positioning of all component fragments.

5. How does the plasmid DNA enter the E. coli cells during transformation?

Getting Plasmid DNA into Bacterial Cells During the transformation process, plasmid DNA successfully enters E. coli cells by temporarily disrupting the normally impermeable bacterial cell membrane, allowing DNA passage through the otherwise sealed barrier.

Two Primary Transformation Techniques Two main strategies accomplish this membrane permeabilization:

1. Heat shock: This method applies a sudden, dramatic shift in temperature, destabilizing the membrane structure and opening transient channels through which DNA can enter.

2. Electroporation: This approach uses a brief, high-voltage electrical pulse that creates temporary microscopic pores across the cell membrane, providing pathways for the plasmid DNA to cross into the cytoplasm. In both cases, the temporary openings allow DNA molecules to pass through the membrane barrier before the cell membrane reseals itself. Post-Transformation Recovery Following successful DNA uptake, the transformed bacterial cells require placement in a nutrient-rich growth medium where they can recover and restore normal membrane integrity. During this recovery period, the cells repair the membrane damage and activate gene expression, including the production of antibiotic resistance proteins encoded by genes on the plasmid. This recovery step is essential for ensuring that successfully transformed cells can survive when exposed to selective conditions. Only cells that have successfully integrated the plasmid will express the antibiotic resistance gene, allowing them to survive and grow on culture plates containing antibiotics, while untransformed cells die.

3. Describe another assembly method in detail (such as Golden Gate Assembly)

   1. Explain the other method in 5 - 7 sentences plus diagrams (either handmade or online).
   2. Model this assembly method with Benchling or Asimov Kernel!

When Golden Gate Assembly Is Advantageous Golden Gate assembly is the preferred cloning method when your DNA sequences already contain Type IIS restriction sites and you need to assemble multiple fragments simultaneously at high efficiency. Unlike Gibson assembly, which offers greater flexibility in fragment design, Golden Gate is specifically optimized for situations where you have many DNA pieces—up to approximately 35 fragments—that need to be joined into a single construct all at once. This method operates as a “one-pot” process, meaning all components react together in a single tube, streamlining the workflow compared to multi-step procedures.

Essential Components and Mechanism Golden Gate assembly requires several key ingredients: (1) DNA fragments obtained from PCR or previously cloned sources, each flanked by Type IIS restriction enzyme recognition sites (such as BsaI or BsmBl); (2) a destination vector—a linearized plasmid backbone containing inward-facing Type IIS recognition sites; (3) Type IIS restriction enzymes (for example, BsaI-HFv2 or BsmBl-v2) that cut DNA at specific recognition sequences; (4) T4 DNA ligase, an enzyme that catalyzes strand joining; (5) reaction buffer to maintain proper chemical conditions; and (6) nuclease-free water as the reaction medium.

The Assembly Process All components are combined in a single reaction tube and then placed in a thermocycler, where the temperature alternates between 37°C (for restriction enzyme cutting) and 16°C (for DNA ligation). This thermal cycling allows cutting and joining to occur sequentially within the same reaction, maximizing efficiency. The key to Golden Gate’s elegance is the Type IIS restriction enzymes, which cut DNA slightly offset from their recognition sequence, generating sticky end overhangs that are perfectly complementary to adjacent DNA fragments. Once cutting is complete, the ligase enzyme seamlessly joins the sticky ends together, assembling all fragments in the correct order and orientation. This approach is particularly powerful for combining multiple genetic elements such as promoters, coding sequences, and regulatory regions. After the thermocycler reaction finishes, purification and bacterial transformation procedures are essentially identical to Gibson assembly.

References

1. The use of LLM to help with finding information and reporting

Week 7: GENETIC CIRCUITS PART II

Week # 7 Genetic Circuits Part II

GENETIC CIRCUITS PART II

To learn neuromorphic genetic circuits, showing how engineered gene networks can implement neural-network “perceptron”-like computation and learning

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions?

To understand the advantages of IANNs (In silico Artificial Neural Networks / Integrated Artificial Neural Networks in synthetic biology) over traditional Boolean genetic circuits, it helps to look at how biological computing is evolving. Traditional genetic circuits act like classic computer chips: they take inputs (like the presence of a specific molecule) and use logic gates (AND, OR, NOT) to produce a definitive, binary ON/OFF response. IANNs, however, mimic the brain’s neural networks using biological components. Here is why IANNs are a massive step up from traditional Boolean genetic circuits:

1. Continuous (Analog) vs. Binary Processing • Traditional Circuits: They are strictly binary. They require sharp thresholds to determine if a signal is a 1 or a 0. This is highly inefficient in biological systems because nature rarely operates in pure black and white. • IANNs: They process analog signals. They can take continuous, graded inputs (e.g., varying concentrations of a toxin or biomarker) and produce a scaled, proportional output. This allows for much more nuanced decision-making, akin to how our own cells actually sense the environment.

2. Scalability and Resource Economy • Traditional Circuits: Scaling up a Boolean circuit requires stacking more and more physical logic gates. In synthetic biology, every new gate requires distinct promoters, repressors, and plasmids. Cells quickly run out of metabolic energy (the “retroactivity” and “resource burden” problem), causing the circuit to crash. • IANNs: They achieve complex computational depth using far fewer biological parts. By adjusting the “weights” of connections (e.g., tuning the binding affinities of a few regulatory proteins), a small network can perform tasks that would require dozens of traditional logic gates.

3. High-Dimensional Pattern Recognition • Traditional Circuits: They struggle with complex pattern recognition. If you want a cell to detect a disease based on a combination of 5 different biomarkers, a Boolean circuit requires a massive, fragile web of nested AND gates. • IANNs: Neural networks excel at fuzzy logic and multi-variate pattern recognition. They can integrate multiple noisy, weak inputs simultaneously, weigh their relative importance, and accurately classify a state (e.g., “Healthy” vs. “Cancerous”) even if one of the biomarkers is slightly off.

4. Robustness to Noise • Traditional Circuits: Biological environments are incredibly noisy. Molecular fluctuations can easily cause a Boolean gate to misfire, flipping a 0 to a 1 and ruining the entire computational chain. • IANNs: Because they are distributed networks, they possess inherent noise-filtering capabilities. The weights and non-linear activation functions average out random biological noise, making the overall system far more robust and less prone to catastrophic failure.

5. Trainability and Reconfigurability • Traditional Circuits: If you want to change the function of a Boolean circuit (e.g., changing it from an AND gate to an OR gate), you usually have to physically re-engineer the DNA sequence, swap out promoters, and rebuild the cell line from scratch. • IANNs: They can theoretically be “trained” or tuned. By subtly adjusting chemical inducers, light exposure (in optogenetic networks), or minor mutation rates, the same basic network structure can be repurposed to map entirely different input/output relationships without a complete structural overhaul.

6. Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal. One of the most promising and impactful applications for an Integrated Artificial Neural Network (IANN) in synthetic biology is autonomous, multi-biomarker cancer diagnostics and targeted drug delivery. In cancer therapy, a major hurdle is that tumor cells are highly adaptive and rarely defined by a single unique marker. Traditional Boolean logic gates require an exact combination of absolute ON/OFF signals (e.g., “Antigen A AND Antigen B must be present”). If a tumor cell downregulates just one antigen, a Boolean circuit fails to detect it. An IANN solves this by acting as an intelligent, cell-based classifier that can process a “fuzzy” spectrum of environmental cues to accurately target tumor microenvironments while sparing healthy tissue. The Application: Smart Living Therapeutics Imagine engineering a patient’s T-cells or a non-pathogenic probiotic bacteria with a genetic IANN. This living therapeutic circulates through the body, constantly sampling the local environment to decide whether it is sitting next to a healthy cell or a malignant tumor. Detailed Input/Output Behavior An IANN mimics a computational neuron mathematically, where the output y is determined by the weighted sum of inputs passed through a non-linear activation function (like a sigmoidal Hill function):

7. The Inputs (Continuous Analog Signals) Instead of binary 1s and 0s, the cell senses a continuous range of concentrations (x_i) via synthetic surface receptors and promoters: • Input 1 (x_1): Hypoxia (Oxygen levels). Tumors are notoriously oxygen-deprived. The input is high when oxygen is low. • Input 2 (x_2): Extracellular Lactate/Acidity. Tumor metabolism generates high levels of lactic acid, lowering local pH. • Input 3 (x_3): Tumor-Associated Antigen (TAA). A surface protein commonly found on the specific cancer, but occasionally present on healthy cells in low amounts.

8. The Internal Processing (Biological Weights) Inside the cell, these inputs trigger the production of specific regulatory proteins. The “weights” (w_i) are physically engineered into the DNA by adjusting the binding affinities of promoters or changing plasmid copy numbers. For instance, if Antigen presence (x_3) is the most reliable indicator, its promoter is engineered to have a high weight, meaning it drives transcription much more aggressively than the hypoxia signal.

9. The Output (Proportional Therapeutic Payload) The final output (y) is the transcription and secretion of a localized therapeutic agent, such as an anti-tumor cytokine (e.g., IL-12) or a targeted toxin. • Traditional Boolean Output: Either 100% drug release or 0% drug release. • IANN Output: Graded and contextual. If the cell detects mild hypoxia and medium acidity, but zero tumor antigen, the network computes a low probability of cancer and releases no drug. If it detects high antigen, high acidity, and high hypoxia, it releases a maximum payload. If it encounters a complex intermediate profile, it secretes a proportional, moderate dose to safely address the threat without causing systemic toxicity. Limitations Faced by Biological IANNs While computationally elegant, implementing an IANN inside a living cell faces severe physical and biological constraints: • The “Static Weight” Problem (No Real-Time Training): In computer software, a neural network learns by adjusting its weights via backpropagation over millions of iterations. In a living cell, you cannot easily “train” the network on the fly. Weights must be meticulously pre-calculated and hardcoded into the DNA architecture via genetic engineering before the cell enters the body. • Metabolic Burden and Resource Competition: Every “node” and “weight” in a genetic neural network requires the host cell to transcribe RNA and translate proteins. If the IANN is too large, it will monopolize the cell’s ribosomes and ATP. The cell will either grow sluggishly, die, or evolutionarily mutate to eject the synthetic circuit entirely. • Biological Crosstalk and Environmental Interference: Unlike clean code, a cell’s interior is packed with native signaling pathways. The synthetic transcription factors used to calculate the IANN’s weights might inadvertently bind to the cell’s native DNA, or native proteins might interfere with the circuit, wildly distorting the pre-calibrated math. • Genetic Instability over Time: Living cells replicate and mutate. Because the IANN provides no survival advantage to the cell itself (only to the patient), natural selection incentivizes the cell to accumulate mutations that break the circuit to save energy.

   3. Below is a diagram depicting an intracellular single-layer perceptron where the X1 input is DNA encoding for the Csy4 endoribonuclease and the X2 input is DNA encoding for a fluorescent protein output whose mRNA is regulated by Csy4. Tx: transcription; Tl: translation.

10. Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2

Assignment Part 2: Fungal Materials

1. What are some examples of existing fungal materials and what are they used for? What are their advantages and disadvantages over traditional counterparts?

Several fungal‑based materials are already being produced or prototyped, mainly using mycelium (the “root” network) or fungal biomass. They are used as biodegradable substitutes for plastics, foams, leather, paper, insulation, and even some construction elements.

Below are concrete examples, their uses, and how they compare to traditional counterparts.

1. Mycelium foam / packaging

• Ecovative Design’s MycoFoam and MycoComposite packaging grown on agricultural waste (e.g., corn husks, hemp).

• Mycelium “bricks” and cushioning blocks for protective packaging, replacing polystyrene (Styrofoam).

  Uses:

• Protective packaging for electronics, fragile goods, and shipping inserts.

• Lightweight insulation or filler in construction panels.

  Advantages vs. polystyrene / plastics:

• Biodegradable and compostable; decomposes in weeks–months instead of centuries.

• Grown at ambient temperature on low‑value waste (sawdust, straw), with low energy and carbon footprint.

• Naturally fire‑resistant and termite‑resistant in some formulations, with good thermal and acoustic insulation.

  Disadvantages:

• More sensitive to moisture and humidity; can lose strength or compress if wet.

• Lower mechanical strength and density than dense plastics; better for cushioning than load‑bearing structures.

• Batch‑to‑batch variability in growth and density can complicate quality control.

2. Mycelium leather alternatives (“myco‑leather”) Examples:

• MycoWorks’ Reishi mycelium leather for bags, shoes, and upholstery.
• Other “myco‑leather” textiles grown as fungal mats on trays (often from species such as Fomes fomentarius or Phellinus spp.).

Uses:

• Fashion (bags, shoes, jackets), furniture upholstery, and interior design.

Advantages vs. animal leather or synthetic leather:

• No livestock rearing; lower land use, methane emissions, and water pollution than bovine leather.
• Often biodegradable or compostable, unlike most synthetic (PU/PVC) leather.
• Can be grown to precise shapes and textures, reducing mechanical cutting waste.

Disadvantages:

• Limited durability and abrasion resistance compared with high‑grade bovine leather at present.
• Cost and scale still higher than bulk synthetic leather; production is not yet at mass‑market polyester‑PU volumes.
• May require coatings or treatments (e.g., for water resistance) that can reduce biodegradability.

3. Fungal‑derived paper‑like materials Examples:

• Fungal “paper” made from liquid fermentation of filamentous fungi (often Trametes or related polypores) into chitin–β‑glucan sheets.
• Early stage materials for printing, filters, and coatings instead of wood‑pulp paper.

Uses:

• Specialty printing surfaces, filtration membranes, and coatings.

Advantages vs. wood‑pulp paper:

• Can be grown from waste streams (e.g., agricultural byproducts) instead of virgin trees.
• Some fungal papers have higher toughness or porosity tailored for specific filtering or biomedical uses.

Disadvantages:

• Not yet cost‑competitive for bulk printing or packaging paper.

• Limited industrial supply chains and standardized processing compared with paper mills.

  4. Mycelium “bricks” and construction panels Examples:

• Mycelium‑bound insulation panels and bricks grown on agricultural residues (hemp, straw, sawdust).

• Acoustic panels from companies such as MOGU using mycelium‑based composites for interior sound‑absorbing surfaces.

Uses:

• Thermal and acoustic insulation in walls, ceilings, and partitioning.
• Interior cladding, acoustic panels, and non‑structural architectural elements.

Advantages vs. mineral wool / expanded polystyrene / concrete:

• Very low embodied energy and carbon‑negative potential when grown on waste biomass.
• Good thermal and acoustic performance per unit weight; lightweight and easy to handle.
• Naturally biodegradable at end‑of‑life, unlike foam or mineral‑wool insulation.

Disadvantages:

• Compressive strength far below concrete (mycelium bricks ~30 psi vs. concrete ~4000 psi).

• Susceptible to moisture and long‑term fungal degradation if not properly sealed.

• Limited load‑bearing capacity restricts use to non‑structural or low‑stress applications.

  5. Other fungal “soft” materials Examples:

• Historical “felt‑like” textiles from polypore fruit bodies (e.g., Fomes fomentarius “amadou” or German Amou), now being revisited for niche textiles and fashion.

• Fungal biomass for food and feed (e.g., Fusarium venenatum “Quorn” mycoprotein), which is a protein material but not usually classed as “structural.”

Uses:

• Specialized textiles, cultural crafts, and decorative materials.
• High‑protein foods and feedstocks.

Advantages vs. cotton / synthetic fibers:

• Can be grown on waste streams with small land and water footprints.
• Natural biodegradability and relatively low chemical input.

Disadvantages:

• Limited mechanical strength and durability compared with conventional textiles.
• Niche supply chains and limited industrial‑scale processing.

References

1. Flexible Fungal Materials: Shaping the Future https://www.sciencedirect.com/science/article/abs/pii/S0167779921000603

2. How Fungi Can Transform Waste Into Useful Materials https://joyfulmicrobe.com/how-fungi-can-transform-waste-into-useful-materials/

3. Will Buildings in the Future Be Built From Mushrooms? - RESET.ORG https://en.reset.org/mycelium-construction-material-benefit/

4. Current Insights in Fungal Importance—A Comprehensive … https://pmc.ncbi.nlm.nih.gov/articles/PMC10304223/

5. Benefits of Fungi for the Environment and Humans https://www.decadeonrestoration.org/stories/benefits-fungi-environment-and-humans

6. Fungi-based materials | Notion https://pbdvc-research.notion.site/Fungi-based-materials-3b088667784f416e90169be831fb6105

7. Growing sustainable materials from filamentous fungi | The Biochemist https://portlandpress.com/biochemist/article/45/3/8/233178/Growing-sustainable-materials-from-filamentous

8. Mycelium-Based Composites - Using Fungi as Building Materials https://www.youtube.com/watch?v=vWkGpbOXZj8

9. Fungus - Wikipedia https://en.wikipedia.org/wiki/Fungus

   2. What might you want to genetically engineer fungi to do and why? What are the advantages of doing synthetic biology in fungi as opposed to bacteria?

One would genetically engineer fungi to enhance or redirect their natural biology for industrial, medical, agricultural, or environmental applications. At the same time, fungi offer several unique advantages over bacteria for synthetic biology, especially for complex molecules and eukaryotic‑style processes.

Below I is an outline on using fungi then contrast fungal synthetic biology with bacterial systems.

What to engineer fungi to do

1. Hyper‑produce natural products and drugs

• Fungi are already rich sources of antibiotics (e.g., penicillin), statins (lovastatin), immunosuppressants (cyclosporine), and anticancer scaffolds.
• Engineer them to:
• Overexpress or “wake up” silent biosynthetic gene clusters that do not normally make detectable compounds.
• Combine pathways from different fungi or other organisms to create hybrid natural products with new bioactivities.

Why: 50% of approved clinical drugs are natural products or derivatives; engineered fungi can accelerate drug discovery and lower production costs.

2. Produce high‑value enzymes and acids

• Filamentous fungi such as Aspergillus niger naturally secrete large amounts of industrial enzymes (amylases, cellulases, proteases) and organic acids (citric, aconitic, itaconic).
• Engineering goals include:
• Overexpressing enzymes for biomass deconstruction (e.g., in biorefineries).
• Redirecting metabolism to increase titers of organic acids used as food additives or chemical‑building blocks.

Why: This enables cheaper, more sustainable routes to bulk chemicals and biocatalysts compared with chemical synthesis.

3. Build advanced materials and mycelium composites

• Mycelium can be engineered to:
• Modify chitin, β‑glucans, or hydrophobic surface proteins to tune water resistance, mechanical strength, and fire retardancy of mycelium bricks, foams, or textiles.
• Express functional proteins (e.g., adhesion peptides, enzymes) to improve integration with other biomaterials or substrates.

Why:Fungal materials are low‑carbon, biodegradable alternatives to plastics and synthetics; genetic control can make them more robust and standardized.

4. Improve biocontrol and plant‑symbiosis traits

• Entomopathogenic fungi (e.g., Beauveria, Metarhizium) can be engineered to:
• Carry insecticidal toxins or plant‑defence elicitors to target pests more selectively.
• Increase UV tolerance or persistence under field conditions.
• Mycorrhizal or endophytic fungi can be tuned to:
• Enhance phosphate/nitrogen uptake, stress tolerance, or pathogen resistance in crops.

Why:This reduces reliance on synthetic pesticides and fertilizers while keeping the system biologically specific.

5. Add “smart” metabolic or sensing functions

• Fungi can be engineered with synthetic circuits for:
• Biosensors that change color or emit light when they detect pollutants, plant pathogens, or soil nutrients.
• Metabolic switches that turn on biodegradation pathways only in the presence of specific contaminants.

Why:Eukaryotic regulation (e.g., chromatin, promoters, secretion) allows more complex, context‑dependent behaviors than simple bacterial toggle switches.

References

1. Gene-editing gets fungi to spill secrets to new drugs - Futurity https://www.futurity.org/gene-editing-fungi-2854282-2/
2. Genetic engineering of fungi now simplified – acib https://www.acib.at/genetic-engineering-fungi-now-simplified/
3. Nature Index Genomic Engineering of Filamentous Fungi for … https://www.nature.com/nature-index/topics/l4/genomic-engineering-of-filamentous-fungi-for-biotechnological-applications
4. Filamentous fungal synthetic biology: Current applications … https://www.sciencedirect.com/science/article/abs/pii/S0734975026001187
5. Systems and Synthetic Biology Approaches to Engineer Fungi … https://pmc.ncbi.nlm.nih.gov/articles/PMC6178918/
6. Genetically Engineering Entomopathogenic Fungi https://pubmed.ncbi.nlm.nih.gov/27131325/
7. Advancing microbial engineering through synthetic biology https://www.jmicrobiol.or.kr/journal/view.php?number=2971
8. Pros and Cons of Synthetic Biology: An Overview https://hudsonlabautomation.com/pros-and-cons-of-synthetic-biology/
9. Fungi in Synthetic Biology Applications | PDF | Fungus https://www.scribd.com/document/511854147/Fungai-Metabolites
10. Application of fungi in genetics | PPTX - Slideshare https://www.slideshare.net/slideshow/application-of-fungi-in-genetics/51498153
11. The use of LLM to help with finding information and reporting

Week 9: CELL FREE SYSTEMS

Week # 9 Cell Free Systems

CELL FREE SYSTEMS

To learn synthesis of proteins using cellular machinery outside of a cell.

General homework questions

1. Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control
2. over experimental variables. Name at least two cases where cell-free expression is more beneficial than cell production.

Cell-Free Protein Synthesis Advantages Cell-free protein synthesis (CFPS) provides substantial benefits compared to conventional cell-based protein production methods, particularly in terms of experimental flexibility and precise control over reaction parameters. In contrast to traditional in vivo approaches that require cell transformation, growth in culture media, and cell disruption, CFPS enables rapid protein production without these intermediate steps, significantly accelerating the research timeline.

Greater Experimental Control and Customization CFPS operates in an open, accessible reaction environment where researchers enjoy extensive freedom to manipulate conditions on the fly. This accessibility allows for straightforward modification of reaction parameters, supplementation with molecular chaperones and folding assistants, incorporation of non-standard amino acids not found in natural proteins, and inclusion of tagged or labeled molecular markers. These capabilities make CFPS exceptionally well-suited for investigating translational control mechanisms, conducting ribosome display experiments for directed evolution, or analyzing intricate protein-protein interaction networks.

Two Primary Applications Where CFPS Excels Producing toxic or cytotoxic proteins: Cell-free systems enable the synthesis of proteins that would be harmful or lethal to living cells if produced inside them. Since there is no intact cellular machinery to be damaged, toxic proteins can be safely manufactured in vitro. Engineering protein sequences with labeled components: CFPS greatly simplifies the incorporation of specialized building blocks, such as amino acids labeled with stable isotopes. This type of customization is vastly more straightforward to accomplish in a cell-free environment compared to achieving the same modifications within living organisms, where metabolic pathways create complications and inefficiencies. In essence, CFPS provides researchers with an unparalleled degree of control and experimental versatility that traditional cell-based systems simply cannot match.

2. Describe the main components of a cell-free expression system and explain the role of each component.

Cell-Free Extract The cell-free extract represents the core biochemical engine of the CFPS system. This liquid extract is derived from disrupted cells and contains the fundamental molecular machinery required for synthesizing proteins—specifically ribosomes (which build proteins), transfer RNAs (which deliver amino acids), enzymes (which catalyze chemical reactions), and numerous regulatory factors. The source organism selected to prepare this extract depends on the characteristics of the target protein: E. coli extracts are commonly used for straightforward proteins, rabbit reticulocyte extracts are chosen when higher eukaryotic protein qualities are needed, and wheat germ extracts are employed for particularly complex or post-translationally modified proteins.

DNA Template The DNA template serves as the genetic blueprint that contains all instructions for creating the desired protein. This template comprises two essential regions: a promoter sequence that signals where transcription should begin and initiate RNA synthesis, and a coding sequence that encodes the precise linear arrangement of amino acids that will form the target protein’s primary structure.

Energy Sources and Essential Cofactors Protein synthesis is an energy-intensive process that demands substantial quantities of adenosine triphosphate (ATP) and guanosine triphosphate (GTP). The CFPS system must continuously regenerate these energy molecules to sustain the reaction and prevent depletion. Beyond these energy sources, metal ion cofactors—particularly magnesium and potassium—are critical for maintaining the chemical environment necessary for both transcription and translation to proceed efficiently and accurately.

3. Why is energy provision regeneration critical in cell-free systems? Describe a method you could use to ensure continuous ATP supply in your cell-free experiment.

The Critical Role of Energy Regeneration Maintaining adequate ATP levels is essential for successful cell-free protein synthesis because adenosine triphosphate serves as the primary energy currency driving both transcription (the conversion of DNA to RNA) and translation (the conversion of RNA to protein). During the CFPS reaction, ATP is consumed rapidly and continuously, as nearly every step of protein synthesis requires energy input. If ATP levels are not actively replenished and maintained, the biochemical machinery will exhaust its energy supply, the reaction will halt, and protein production will plummet significantly.

ATP Regeneration Strategy in E. coli Systems To overcome this energy limitation, E. coli-based cell-free systems employ a coupling strategy using two key components: phosphoenolpyruvate (PEP) and the enzyme pyruvate kinase. This enzymatic pair works together to continuously regenerate ATP from adenosine diphosphate (ADP), ensuring a steady supply of fresh energy molecules throughout the reaction. By maintaining consistent ATP availability through this regeneration mechanism, the system can sustain extended and robust protein synthesis, preventing the energy depletion that would otherwise terminate the reaction prematurely and severely reduce overall protein yield. In essence, energy regeneration transforms CFPS from a short-lived, low-yield process into a sustainable, high-productivity system capable of extended protein manufacturing.

4. Compare prokaryotic versus eukaryotic cell-free expression systems. Choose a protein to produce in each system and explain why.

Prokaryotic cell-free systems (e.g., E. coli extracts) are typically faster to produce protein because transcription and translation are closely coupled, while eukaryotic systems separate these processes and are generally slower. Prokaryotic extracts often yield higher total protein amounts and are less expensive to run than eukaryotic extracts. Eukaryotic cell-free systems (e.g., rabbit reticulocyte, wheat germ, insect, or mammalian extracts) better support complex post-translational modifications such as glycosylation and disulfide bond formation. Prokaryotic systems lack most native eukaryotic chaperones and modification enzymes, which can limit correct folding and activity of many eukaryotic proteins. Eukaryotic systems can more accurately translate mRNAs with complex regulatory elements (e.g., internal ribosome entry sites, Kozak sequences) and handle eukaryotic codon usage more naturally. Prokaryotic systems are more amenable to genetic and biochemical optimization (e.g., energy regeneration, component supplementation) for high-throughput screening and rapid prototyping. Eukaryotic extracts are preferred for membrane proteins and multi-subunit complexes when proper folding, assembly, or membrane insertion requires eukaryotic-specific factors or microsomes. Prokaryotic systems often require codon optimization and may produce inclusion bodies or inactive protein for eukaryotic targets without additional folding aids. Eukaryotic systems are generally costlier, have lower batch-to-batch yields, and can be more variable, but they increase the likelihood of obtaining functionally active eukaryotic proteins. Choice depends on the goal: use prokaryotic systems for speed, yield, and cost-effective screening; use eukaryotic systems when native folding, modifications, or activity are required.

Recommended Proteins and Rationales

Prokaryotic System: Green Fluorescent Protein (GFP) Why GFP is ideal for E. coli CFPS: GFP is a small, robust protein (~27 kDa) that folds autonomously without requiring post-translational modifications or extensive chaperone assistance. The chromophore (fluorescent group) forms spontaneously through auto-oxidation of its own amino acid sequence, making it self-contained and independent of cellular machinery. The rapid synthesis capability of prokaryotic systems would allow researchers to quickly produce labeled protein variants for high-throughput screening, fluorescence assays, or incorporation of non-canonical amino acids at specific positions to tune optical properties. The low cost and ease of E. coli extract preparation make this system economically practical for producing large quantities needed for research or diagnostic applications. Additionally, GFP doesn’t require disulfide bonds or glycosylation, which are unnecessary complications in a prokaryotic environment.

Eukaryotic System: Antibody (Immunoglobulin) Why antibodies are ideal for eukaryotic CFPS: Antibodies are large, multimeric proteins (~150 kDa per molecule, often functioning as dimers or larger complexes) that require sophisticated post-translational processing. They contain multiple disulfide bonds that stabilize the structure and are critical for antigen binding and immune function—a feature that eukaryotic systems handle naturally through their oxidizing endoplasmic reticulum environment. Antibodies also undergo glycosylation at specific sites, which is essential for their effector functions (complement activation, antibody-dependent cellular cytotoxicity, and Fc receptor binding). The rich folding environment provided by eukaryotic extracts, with resident molecular chaperones like BiP and protein disulfide isomerases, ensures proper tertiary and quaternary structure formation. While eukaryotic CFPS is more expensive and time-consuming, the production of functional, correctly modified antibodies justifies this investment for therapeutic development, diagnostic reagents, or research applications where authentic biological activity is non-negotiable.

Summary Choose prokaryotic systems for rapid, simple, cost-effective production of robust proteins that self-fold and don’t require post-translational modifications. Choose eukaryotic systems when investing in complex proteins that demand sophisticated folding assistance, disulfide bond formation, or chemical modifications essential for biological function. The choice ultimately depends on balancing speed and cost against biological complexity and functional requirements of your target protein.

Prokaryotic cell-free expression systems, such as those based on E. coli, are fast, cost-effective, and capable of producing high protein yields. However, they lack the ability to perform post-translational modifications and often struggle with proper folding of complex or membrane-bound proteins. In contrast, eukaryotic systems like wheat germ extract are slower and more expensive but offer better support for folding and modifications, making them suitable for expressing complex eukaryotic proteins. For a prokaryotic system, I would choose to express

5. How would you design a cell-free experiment to optimize the expression of a membrane protein? Discuss the challenges and how you would address them in your setup.

6. Imagine you observe a low yield of your target protein in a cell-free system. Describe three possible reasons for this and suggest a troubleshooting strategy for each.

Homework question from Kate Adamala

Design an example of a useful synthetic minimal cell as follows:

1. Pick a function and describe it. a. What would your synthetic cell do? What is the input and what is the output? b. Could this function be realized by cell-free Tx/Tl alone, without encapsulation? c. Could this function be realized by genetically modified natural cell? d. Describe the desired outcome of your synthetic cell operation.
2. Design all components that would need to be part of your synthetic cell. a. What would be the membrane made of? b. What would you encapsulate inside? Enzymes, small molecules. c. Which organism your Tx/Tl system will come from? Is bacterial OK, or do you need a mammalian system for some reason? (hint: for example, if you want to use small molecule modulated promotors, like Tet-ON, you need mammalian) d. How will your synthetic cell communicate with the environment? (hint: are substrates permeable? or do you need to express the membrane channel?)
3. Experimental details a. List all lipids and genes. (bonus: find the specific genes; for example, instead of just saying “small molecule membrane channel” pick the actual gene.) b. How will you measure the function of your system?

AHL-Quenching Synthetic Cell for Biofilm Disruption What the Cell Does This engineered synthetic minimal cell detects the quorum-sensing molecule 3-oxo-C12-HSL, which is released by the pathogenic bacterium Pseudomonas aeruginosa. When exposed to this signal, the cell produces the enzyme AiiA (a lactonase), which breaks down the AHL molecules within the enclosed vesicle. As new AHL continuously enters from the surrounding environment, the trapped enzyme works to destroy it, establishing a depletion zone that gradually reduces the available AHL in the nearby area. This reduction in quorum-sensing signaling dampens the pathogen’s ability to express disease-causing proteins and build biofilm structures.

Input and Output Input: The AHL signal (3-oxo-C12-HSL) from the surrounding liquid. Output: Reduced AHL concentration in the environment; the lactonase enzyme stays trapped inside the vesicle and does not escape. The primary measurable indicator is a decline in AHL levels over time.

Feasibility of Alternative Realizations Using only cell-free transcription-translation in solution: This approach is technically possible—mixing DNA and AHL in a test tube would produce AiiA and allow degradation. However, without encapsulation, the system lacks physical boundaries, the enzyme becomes diluted and exposed, and it cannot be precisely delivered to an infection site. A vesicle creates a controlled, protected microreactor that can be positioned exactly where treatment is needed. Using a genetically engineered living bacterium: An engineered E. coli strain programmed to express the LasR receptor and AiiA enzyme would theoretically accomplish the same sensing and degradation. However, living microorganisms present significant concerns: they may multiply uncontrollably, share modified genes with other bacteria, and face regulatory approval obstacles. A synthetic cell avoids these risks because it is non-living, incapable of reproduction, and inherently safer for medical use.

Desired Outcome Introducing a collection of these synthetic vesicles into a P. aeruginosa biofilm would lower AHL concentrations in that region to below the level needed for quorum-sensing activation. This would suppress the production of harmful proteins (such as elastase and pyocyanin) and destabilize the biofilm structure, making the bacteria vulnerable to destruction by the immune system or antimicrobial drugs.

Design of the Synthetic Cell Membrane Composition The synthetic cell consists of a single-layer lipid sphere containing: • POPC (70% of lipid composition) – promotes fluid membrane behavior and is compatible with living tissue • Cholesterol (30% of lipid composition) – strengthens the membrane structure and controls how easily molecules pass through The AHL molecule is sufficiently lipid-soluble (logP ≈ 3.5) to naturally seep through the lipid barrier without requiring specialized transport proteins. Encapsulated Components Biological machinery: An E. coli cell-free system that includes the natural RNA polymerase (with σ⁷⁰ factor), ribosomes, and all components needed for protein synthesis. This bacterial system is ideal because it recognizes standard bacterial promoters without needing more complex mammalian machinery. Fuel and building blocks: Energy sources (PEP, ATP, GTP, UTP, CTP), amino acids, transfer RNA, nucleotides, creatine phosphate, and necessary salts—all standard components for keeping bacterial lysate functional. Genetic instructions: Two circular DNA plasmids:

1. pLuxR – continuously produces the LasR receptor protein using a powerful σ⁷⁰ promoter (J23119), with the lasR gene from  P. aeruginosa PAO1 (GenBank reference: NP_250121.1)
2. pAiiA produces the AiiA lactonase enzyme in response to AHL binding, controlled by the lasI promoter region (which contains the DNA binding site for the LasR-AHL complex) and containing the aiiA gene from Bacillus sp. 240B1 (GenBank reference: AAF62398.1) (These can be placed on separate plasmids for flexible tuning or combined on a single plasmid.)

Interaction with Surroundings AHL freely passes through the membrane via passive diffusion; no special channels are required. By contrast, the lactonase protein cannot escape because it carries no export signal and the membrane pores are too small (~0.5 nanometers) for proteins to exit. This design confines the synthetic cell to act purely as an AHL sink, eliminating any release of the enzyme into the environment.

Experimental Details Key Genetic and Chemical Components Component Specification POPC 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine Cholesterol Cholest-5-en-3β-ol Promoter (constitutive) J23119 (Anderson collection) lasR gene Pseudomonas aeruginosa PAO1, locus PA1430, protein NP_250121.1 PlasI promoter ~200 base pairs upstream of lasI (PA1432), includes las box binding site aiiA gene Bacillus sp. 240B1 lactonase, GenBank reference AAF62398.1

Measuring Performance Testing AHL removal from the environment: Expose a known quantity of synthetic vesicles to 1–5 µM of 3-oxo-C12-HSL in a buffered solution. At regular time points, collect samples of the surrounding liquid and measure remaining AHL using either liquid chromatography–mass spectrometry (LC-MS/MS) or a bacterial biosensor (E. coli carrying lasR and a fluorescent reporter gene under the lasI promoter). A decline in fluorescence demonstrates the quenching effect. Confirming enzyme activity inside the vesicle: Break open vesicles using freeze-thaw cycles and assess how efficiently AiiA degrades AHL by employing a color-changing dye that reacts to the acid released when lactones hydrolyze, or by using a synthetic lactone compound linked to a fluorescent tag (e.g., carboxyfluorescein) and monitoring the fluorescence change. Evaluating impact on the pathogen: Combine the synthetic cells with live P. aeruginosa PAO1 and assess whether virulence markers (elastase activity, pyocyanin pigment levels) decrease or whether the biofilm becomes thinner or less dense (using crystal violet staining). This approach creates a clearly defined, non-replicating, safe therapeutic system that uses a minimal lipid compartment and standard bacterial protein-making machinery to neutralize a pathogenic communication pathway.

Homework question from Peter Nguyen

Freeze-dried cell-free systems can be incorporated into all kinds of materials as biological sensors or as inducible enzymes to modify the material itself or the surrounding environment. Choose one application field — Architecture, Textiles/Fashion, or Robotics — and propose an application using cell-free systems that are functionally integrated into the material. Answer each of these key questions for your proposal pitch: • Write a one-sentence summary pitch sentence describing your concept. • How will the idea work, in more detail? Write 3-4 sentences or more. • What societal challenge or market need will this address? • How do you envision addressing the limitation of cell-free reactions (e.g., activation with water, stability, one-time use)?

BioChroma—Biosensor in Enhanced Textiles for Athlete and Worker Health Monitoring Product Concept BioChroma is an innovative textile technology that incorporates freeze-dried, cell-free biosensing components directly into fabric fibers. The system undergoes a lasting color shift when it encounters specific sweat-based health indicators, enabling athletes and industrial workers to monitor hydration status and electrolyte balance in real time without needing electronic devices or specialized equipment.

Operational Mechanism The system uses freeze-dried protein-synthesis machinery equipped with regulatory proteins that respond to specific molecular triggers and genes that encode color-producing proteins. These components are packaged into tiny droplets suspended within a gel and then applied as a coating to sections of fabric designed to absorb moisture. When sweat contacts the fabric, it rehydrates the dormant system, which then activates a series of reactions that identify target molecules (such as lactate or sodium) and rapidly manufacture a colored pigment—all without needing living microorganisms or electrical input. The chemical cascade is engineered to be one-directional and irreversible, ensuring that the color change becomes a permanent marker of the wearer’s peak stress levels rather than a temporary indicator. By placing several different sensor zones at various locations on a single garment, the design allows simultaneous tracking of multiple different biomarkers from different areas of the body.

The Problem and Market Opportunity This technology tackles an important unmet need: many outdoor workers, military personnel, and endurance athletes are vulnerable to heat-related medical emergencies but lack practical access to real-time body monitoring systems. Traditional electronic smart textiles are expensive, fragile, depend on batteries, and may violate workplace safety rules. Additionally, these devices deteriorate when laundered and generate electronic waste. The biological approach presented here offers a compelling alternative—significantly cheaper, single-use, and completely compostable—making physiological monitoring accessible to broad consumer populations seeking practical health tracking options.

Overcoming Technical Challenges with Cell-Free Systems To prevent unwanted activation and maintain long-term viability, the freeze-dried biosensing material is enclosed in a water-repellent silica coating that dissolves only when exposed to sweat conditions (particular pH and salt concentration), guarding against false activation from rain or everyday moisture and allowing the product to remain stable for more than two years at normal temperatures. The one-time-use nature of the system is reframed as an intentional advantage, functioning similarly to radiation exposure badges that accumulate and record total stress over time. A modular patch architecture means users can replace only the sensors that have been activated rather than discarding the entire garment. Additionally, the dried biological material is treated with protective compounds (trehalose sugar and stabilizing proteins) that preserve enzyme function even after the fabric experiences physical stress and repeated washing before the sensors are triggered.

Homework question from Ally Huang

Freeze-dried cell-free reactions have great potential in space, where resources are constrained. As described in my talk, the Genes in Space competition challenges students to consider how biotechnology, including cell-free reactions, can be used to solve biological problems encountered in space. While the competition is limited to only high school students, your assignment will be to develop your own mock Genes in Space proposal to practice thinking about biotech applications in space! For this particular assignment, your proposal is required to incorporate the BioBits® cell-free protein expression system, but you may also use the other tools in the Genes in Space toolkit (the miniPCR® thermal cycler and the P51 Molecular Fluorescence Viewer). For more inspiration, check out https://www.genesinspace.org/ .

1. Provide background information that describes the space biology question or challenge you propose to address. Explain why this topic is s ignificant for humanity, relevant for space exploration, and scientifically interesting. (Maximum 100 words)
2. Name the molecular or genetic target that you propose to study. Examples of molecular targets include individual genes and proteins, DNA and RNA sequences, or broader -omics approaches. (Maximum 30 words)
3. Describe how your molecular or genetic target relates to the space biology question or challenge your proposal addresses. (Maximum 100 words)
4. Clearly state your hypothesis or research goal and explain the reasoning behind it. (Maximum 150 words)
5. Outline your experimental plan - identify the sample(s) you will test in your experiment, including any necessary controls, the type of data or measurements that will be collected, etc. (Maximum 100 words)

Cell-Free Biosensing for Radiation Damage Assessment in Space Background Astronauts traveling beyond Earth’s protective magnetic field are exposed to high-energy cosmic radiation that causes harmful changes to DNA, elevating their lifetime risk of malignancy and cellular deterioration. Directly evaluating the biological impact of this damage in living cells is challenging, particularly during space missions where resources are limited. Cell-free protein-making systems such as BioBits® offer a practical solution: they enable rapid measurement of how radiation-damaged DNA impairs the production of mRNA and proteins, creating a simple, lightweight detection method for monitoring genetic damage during extended journeys through deep space.

Molecular or Genetic Target The experimental system uses a superfolder GFP (sfGFP) gene carried on a plasmid, which functions as a fluorescent signal indicating how efficiently genes are transcribed and translated after the DNA has been damaged by radiation.

Connection to Space Biology Challenge Radiation damage creates breaks and chemical alterations in the DNA backbone that obstruct the movement of RNA polymerase enzymes, thereby suppressing mRNA production. When researchers introduce radiation-damaged sfGFP DNA into a BioBits reaction mixture, the reduced fluorescence signal directly correlates with the proportion of DNA templates that have been rendered non-functional. This approach translates the amount of DNA injury into a measurable decrease in gene expression, resembling what occurs when critical genes are damaged in the cells of actual space travelers.

Research Hypothesis and Objectives The research team predicts that plasmids exposed to radiation mimicking galactic cosmic ray exposure will generate diminished amounts of sfGFP protein in a cell-free reaction system, with the reduction increasing proportionally to radiation dose. Since BioBits reactions contain all necessary enzymes for transcription and translation but lack the DNA repair machinery found in live cells, any reduction in fluorescence must come directly from physical damage to the DNA template itself. By plotting the relationship between radiation dose and fluorescence loss, this assay will establish a reliable, space-suitable method for quantifying how radiation affects gene expression capability, potentially enabling continuous health assessment during long-term space missions.

Experimental Approach Identical samples of isolated pSFGFP plasmid will be exposed to iron ion (⁵⁶Fe) radiation at four levels: no radiation (as a control), 5 gray, 10 gray, and 20 gray. Each radiation-treated DNA sample (100 nanograms per reaction) will then be mixed into reconstituted BioBits components and allowed to incubate for two hours at 30 degrees Celsius. Fluorescence intensity will be measured at the end of the reaction using a P51 Molecular Fluorescence Viewer. The experiment will include three identical reactions per radiation dose plus a blank reaction containing no DNA to serve as a background reference. The resulting fluorescence measurements will be plotted against radiation dose to establish a curve showing the progressive decline in gene expression as DNA damage increases.

References

1. The use of LLM to help with finding information and reporting

Week 10: IMAGING AND MEASUREMENT

Week # 10 Imaging and Measurement

IMAGING AND MEASUREMENT

To learn a range of advanced technologies to do precision measurement of proteins at atomic scales, characterizing chemical composition, and detecting protein sequence and structure.

Homework: Waters Part I — Molecular Weight

We will analyze an eGFP standard on a Waters Xevo G3 QTof MS system to determine the molecular weight of intact eGFP and observe its charge state distribution in the native and denatured (unfolded) states. The conditions for LC-MS analysis of intact protein cause it to unfold and be detected in its denatured form (due to the solvents and pH used for analysis). 1. Based on the predicted amino acid sequence of eGFP (see below) and any known modifications, what is the calculated molecular weight? You can use an online calculator like the one at https://web.expasy.org/compute_pi/ eGFP Sequence: MVSKGEELFTG VVPILVELDG DVNGHKFSVS GEGEGDATYG KLTLKFICTT GKLPVPWPTL VTTLTYGVQC FSRYPDHMKQ HDFFKSAMPE GYVQERTIFF KDDGNYKTRA EVKFEGDTLV NRIELKGIDF KEDGNILGHK LEYNYNSHNV YIMADKQKNG IKVNFKIRHN IEDGSVQLAD HYQQNTPIGD GPVLLPDNHY LSTQSALSKD PNEKRDHMVL LEFVTAAGIT LGMDELYKLE HHHHHH Note: This contains a His-purification tag (HHHHHH) and a linker (the LE before it).

     The molecular weight is 27875.41 Da 

2. Calculate the molecular weight of the eGFP using the adjacent charge state approach described in the recitation. Select two charge states from the intact LC-MS data (Figure 1) and:
    1. Determine  for each adjacent pair of peaks  using:

Let’s choose two adjacent peaks: • m/z₁ = 1000.4947 • m/z₂ = 1037.4927 The molecular weight is 27875.41 Da These represent two charge states: z and z+1

Week 11: BUILDING GENOMES

Week # 11 Building Genomes

BUILDING GENOMES

To inspire collaboration and creativity while designing a scientifically rigorous cell-free fluorescent protein optimization experiment together.

Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork

1. Contribute at least one pixel to this global artwork experiment before the editing ends on Sunday 4/19 at 11:59 PM EST.
    ◦ A personalized URL was sent to the email address associated with your Discourse account, and you can discuss the artwork on the Discourse.

https://rcdonovan.com/synbiobeta I contributed 3 on the in the middle of the artwork

    ◦ If you did not have a chance to contribute, it’s okay, just make sure you become a TA this fall! 😉
2. Make a note on your HTGAA webpages including:
    ◦ what you contributed to the community bioart project (e.g., “I made part of the DNA on the bottom right plate”)
    ◦ what you liked about the project, and
    ◦ what about this collaborative art experiment could be made better for next year.

Part B: Cell-Free Protein Synthesis | Cell-Free Reagents

  Referencing the cell-free protein synthesis reaction composition (the middle box outlined in yellow on the image above, also listed below), provide a 1-2 sentence description of what each component’s role is in the cell-free reaction.

E. coli Lysate

• BL21 (DE3) Star Lysate (includes T7 RNA Polymerase): - Provides the cellular machinery needed for transcription and translation, including ribosomes, elongation factors, enzymes, and T7 RNA polymerase to drive expression from T7 promoters.

Salts and Buffer

• Potassium Glutamate: - Maintains ionic strength and osmotic balance, helping transcription and translation machinery function properly. -HEPES-KOH pH 7.5: - Buffers the reaction to keep pH stable, which is important for enzyme activity and protein synthesis.
• Magnesium Glutamate: Supplies magnesium, a key cofactor that stabilizes ribosomes, nucleic acids, and many reaction enzymes.
• Potassium phosphate monobasic: Contributes phosphate buffering and can help support energy-related chemistry in the reaction.
• Potassium phosphate dibasic: Works with the monobasic form to set and maintain the desired pH and buffering capacity.

Energy and Nucleotides

• Ribose: Provides a sugar backbone component for nucleotide-related chemistry and can support metabolic regeneration pathways.
• Glucose: Serves as an energy source to help regenerate ATP and sustain the reaction.
• AMP: Participates in energy metabolism and regeneration pathways that help maintain usable nucleotide pools.
• CMP: Supports the nucleotide pool needed for RNA synthesis.
• GMP: Supports the nucleotide pool needed for RNA synthesis.
• UMP: Supports the nucleotide pool needed for RNA synthesis.
• Guanine: Contributes to nucleotide salvage and replenishment of GTP-related pools.

Translation Mix

• 17 Amino Acid Mix: Supplies the protein-building substrates needed for translation into the target protein.
• Tyrosine: Provides an additional amino acid component for protein synthesis.
• Cysteine: Provides an additional amino acid component for protein synthesis and can be important for protein structure via disulfide bonds.

Additives

• Nicotinamide: Supports NAD-related metabolic recycling, helping sustain energy generation in the reaction.

Backfill

• Nuclease Free Water: Brings the reaction to final volume while avoiding nucleases that could degrade DNA or RNA templates.

1. Describe the main differences between the 1-hour optimized PEP-NTP master mix and the 20-hour NMP-Ribose-Glucose master mix shown in the Google Slide above. (2-3 sentences)

The main difference is that the PEP-NTP master mix is designed for a fast, short reaction and uses phosphoenolpyruvate as the primary energy source, while the NMP-Ribose-Glucose master mix is built for a longer reaction and uses simpler metabolic substrates to sustain activity over many hours. In practical terms, the 1-hour mix favors rapid output, whereas the 20-hour mix favors longer-lasting protein synthesis and template usage.

Energy strategy

PEP-based systems usually generate energy more directly and quickly, which helps drive early, high-rate transcription and translation. NMP-Ribose-Glucose systems rely more on gradual metabolic recycling and downstream regeneration, so they tend to support a longer reaction window.

Reaction duration

The 1-hour optimized mix is tuned for speed, so it is useful when you want a quick readout. The 20-hour mix is tuned for persistence, so it is better when you want prolonged expression or higher total yield over time.

Practical implication

If you need a fast assay or screening result, the PEP-NTP mix is usually the better fit. If you need the reaction to keep running much longer, the NMP-Ribose-Glucose mix is the more suitable choice.

2. Bonus question: How can transcription occur if GMP is not included but Guanine is? Transcription can still occur because guanine is a precursor, not the RNA building block itself. In the lysate, enzymes can convert guanine into GMP, then to GDP and GTP, and GTP is the actual nucleotide that RNA polymerase uses to build RNA.

Why guanine is enough

Free guanine can be salvaged into the nucleotide pool through the cell-free extract’s metabolic enzymes, so the reaction does not need GMP to be added separately. Once GMP is made, it can be phosphorylated to the triphosphate form needed for transcription.

What transcription needs

RNA synthesis requires the four ribonucleoside triphosphates: ATP, CTP, GTP, and UTP. So the important point is not whether GMP is added directly, but whether the system can maintain enough GTP availability for RNA polymerase to work.

In your mix

The presence of guanine suggests the master mix is designed to replenish guanine nucleotides metabolically rather than supplying GMP outright. That is consistent with a longer-running cell-free system that relies on internal recycling and salvage pathways.

Part C: Planning the Global Experiment | Cell-Free Master Mix Design

Given the 6 fluorescent proteins we used for our collaborative painting, identify and explain at least one biophysical or functional property of each protein that affects expression or readout in cell-free systems. (Hint: options include maturation time, acid sensitivity, folding, oxygen dependence, etc) (1-2 sentences each)

sfGFP

mRFP1

mKO2

mTurquoise2

mScarlet_I

Electra2

The amino acid sequences are shown in the HTGAA Cell-Free Benchling folder.

Cell-free protein synthesis (CFPS) offers a unique environment for expressing fluorescent proteins (FPs). Because these systems lack the complex regulatory machinery of a living cell, the intrinsic biophysical properties of the protein itself—such as how fast it folds or how much oxygen it requires—become the primary drivers of the visible signal.

1. sfGFP (Superfolder GFP)

Property: Folding Robustness

sfGFP was specifically engineered to resist misfolding, allowing it to reach its functional state even when fused to poorly folding proteins or expressed at high speeds in cell-free systems. This makes it the “gold standard” for CFPS because it ensures that nearly all translated protein becomes fluorescently active rather than forming insoluble aggregates.

2. mRFP1 (Monomeric Red Fluorescent Protein 1)

Property: Maturation Time

As a first-generation monomeric red FP, mRFP1 suffers from a relatively slow maturation time (often exceeding one hour). In cell-free reactions, this creates a significant “time lag” between protein synthesis and signal detection, which can obscure the real-time dynamics of genetic circuits.

3. mKO2 (Monomeric Kusabira Orange 2)

Property: Acid Sensitivity (pK_a)

mKO2 is sensitive to pH changes, with a pK_a of approximately 5.0. While cell-free buffers are usually stable, the accumulation of metabolic byproducts (like organic acids) during long reactions can drop the pH, potentially quenching the mKO2 signal and leading to an underestimation of protein yield.

4. mTurquoise2

Property: Quantum Yield (Brightness)

mTurquoise2 possesses an exceptionally high quantum yield (0.93), making it one of the brightest cyan FPs available. This high intrinsic brightness allows for a very high signal-to-noise ratio in cell-free readouts, which is critical when working with low-yield reactions or microfluidic droplets where the total amount of protein is minimal.

5. mScarlet-I

Property: Maturation Kinetics

mScarlet-I is designed for high brightness and fast maturation (t_{1/2} \approx 36 minutes), which is significantly faster than many other red FPs. This rapid maturation makes it an ideal reporter for cell-free systems where users need to observe red fluorescence almost immediately after the start of translation.

6. Electra2

Property: Oxygen Dependence / Fast Maturation

Electra2 was specifically developed for ultra-fast readouts in time-resolved applications. Its primary functional advantage in cell-free systems is its optimized maturation speed, which minimizes the delay in signal acquisition for high-throughput screening and the characterization of rapid transcriptional-translational (TX-TL) bursts.

sfGFP folds rapidly and efficiently even under suboptimal conditions due to its superfolder mutations, making it ideal for cell-free systems where chaperone activity is limited. Its low pKa (around 3.1) ensures stable fluorescence across a wide pH range typical in these reactions.

mRFP1 has a relatively slow maturation time (around 60 minutes), which delays fluorescence readout in time-sensitive cell-free assays. It also shows moderate folding efficiency, potentially leading to lower yields in crowded lysate environments.

mKO2 exhibits fast maturation and high quantum yield (0.62), allowing quick orange fluorescence detection in cell-free setups. However, its slightly higher pKa (5.5) makes it more pH-sensitive than GFP variants, risking signal loss if the reaction acidifies.

mTurquoise2 matures very rapidly (half-time ~33 minutes) with minimal acid sensitivity (pKa 3.1), providing bright cyan signal early and reliably in cell-free transcription-translation. Its monomeric state prevents aggregation issues that plague some FPs in cell-free crowding.

mScarlet_I offers extremely fast maturation (<15 minutes reported in literature) and high photostability, enabling high-throughput cell-free screening with red-shifted emission. Its optimized folding minimizes misfolding in oxygen-variable cell-free conditions.

Electra2 (a newer far-red FP) shows oxygen-independent chromophore formation like other iLOV-derived proteins, crucial for anaerobic cell-free reactions. Its rapid folding supports expression monitoring without oxygenation dependence that hampers traditional FPs.

References

1. The use of LLM models in research and reporting