Week 1 HW: Principles and Practices

1. Biological Engineering Application:

The Concept: DermLogic

I propose a smart biopolymer hydrogel patch that is designed to treat recalcitrant cutaneous HPV.

• The Problem: Traditional treatment like Cidofir are effective but limited by poor penetration and systemic toxicity risks.
• The Solution: A patch that acts as a local manufacturing unit, producing therapeutics only when specific triggers are met.

Personal Motivation

My commitment to this project stems from watching close friends and family struggle with recalcitrant cutaneous HPV for decades. Despite enduring numerous rounds of painful cryotherapy and laser treatments, their condition remains unchanged. I am struck by the stagnation in treatment options for such a common and stubborn virus. Through this course, I want to bridge that gap by developing a localized, non-invasive solution that offers hope to those who have exhausted traditional medical paths

Learning Sandbox Goals

• Protein Design: I am really interested in learning tools like Alphafold and rosetta to design peptides that can specifically degrade the HPV-specific oncoproteins.
• Designing genetic circuits that use logic to ensure precision delivery.
• I love the idea of Living Materials that act like mini computers embedded into the biopolymer to produce the antiviral peptide on demand.

2. Governance Policy Goals:

From reading the resource material and this weeks presentations I think to ensure a technology like this to be developed ethically, here I propose three primary policy goals:

Safety & Security:

Prevent the accidental release of potent antivirals or engineered microbes out of the living material to unintended places.

• Treating patient safety as paramount. So the first trials will always be done under medical supervision.
• Making the product as inert as possible when not in use and easily biodegradable.

Equity and Autonomy:

In the longer run, the patch needs to be affordable and self-administration capable. This will reduce expensive clinic visits which currently limits access to HPV care.

• Facing the shame associated with such conditions head on and help patients feel understood and cared for. Making information public is the first step towards that.

Constructive Use:

Prevent the genetic circuit delivery system to be used by bad players to deliver harmful compounds.

• As with the fields half pipe of doom these technologies must be steered away from bad actors. But since learning to tame fire humans have managed to live with dangerous but useful things. So keeping an optimists perspective will help be a step ahead of evil.

3. Proposed Governance Action:

(Three options)

i: Cell-free Mandate:

This requires all biological machinery on peoples skin to exist in a cell free state to avoid mishap.

• Use cell free systems that come alive under the right condition.
• There should be a clear On and Off state trigger and indication that is mandatory to perform in order to use the polymer.

ii: Open Source Design Ledger:

Create a local registry of peptides used in the genetic circuits.

• Allow for a broader collaboration platform that allows various participants in the building of such smart biomaterials.

iii: Tiered access control:

Limits access to high potency therapeutics to certified labs while keeping biopolymer open-source

• Learn from existing methodologies and governance practices that deal with sensitive and potentially harmful information and build on top of them.

Current actors interested in this will be researchers who already work with antivirals but might not be native to synthetic biology tools. I would like to share MVPs with them to understand there safety concerns and whether they think my design has any flaws. Also learning about local biosafety laws during the design of the MVP will be paramount. Also health care providers and industry manufacturers of such antivirals would be good collaboration partners.

5. Prioritization:

Thinking about the scores achieved by the proposed governance actions against this rubric I have found that the cell-free mandate should be prioritized. My learnings from this weeks class is that we should prioritize safety by design during every step of the DBTL cycle. This consecutively will allow broader open source collaboration on the biopolymer itself since harm is reduced from step 1. Throughtout the week I came up with a number of biopolymer and syn bio ideas ideas and did the governance rubric on each of them. These ideas included making a tennis string out of proteins, keratin based insulation material that has mold prevention immobilized enzymes, recreating breast milk to replace inadequate infant formula, using synbio to clearn the worlds waters through living synbio mats, programmable cambium. This weeks reframing taught me that this technology is still at its nascent stage and needs careful administration into crucial gaps in human requirements. Every thing we do changes the perception of syn bio to people. So we dont just want to present it as a novelty but an essential tool for the future of life. We must not only think about doing things that are possible but do them while keeping saftey, environment and access a priority. As much as we would like to replace petrochemicals with biopolymers straightaway there use is currently highly specialised. Also using it to solve a perosonally inspired project will make sure that I keep safety and access paramount in my mind. And through every stage of the project, design the safety before hand and not treat it as an impediment just to jump regulatory hoops. The designers of the biology must also be the designers of safety around that biology. Also I would say the trade off of making tiered access could cause some friction but having cell-free will make sharing this information open source more robust and possible.

Thanks to this new framework of thinking I will try to incorporate my bold claims here into practice in my project.

Week 2 Lecture Prep: Q&A

Homework Questions from Professor Jacobson:

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

Answer:As mentioned in the slides it appears that the error rate of DNA Polymerase is $10^{6}$ meaning there will be approximately 1 error every $10^{6}$ base additions. Based on a quick search the length of the human genome seems to be around 3 billion pairs $3\times10^{9}$ bp. So based on that there should be 3000 errors per cell division. So to avoid this DNA polymerase has a built in proof reading that corrects some of these errors. Further research shows that there are additional repair mechanisms that brings the final error to one per $10^{10}$ bases. Later in a slide we also learn about the MutS Repair System found in all DNA.

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?

Answer: DNA nucleotide code codes for amino acids in triplet codons. Since there are four neuclotides it makes it 64 codons (4x4x4) that code for 20 amino acids. This gives each amino acid around 3 possible codon options. Since an average human protein is around 400 aa long that gives each protein $3^{400}$ possibilities of codon options for every protein. That is equal to $10^{190}$ which is more than the atoms in the universe which is $10^{80}$ . This is due to the combination of reasons. One of them being the amount of GC neuclotide pairs which make stronger bonds than AT pair. So nature needs to balance the amount of these GC pairs. Too few and the resulting structure is unstable and gets degraded and too high means excessive folding making the DNA/RNA innaccessible for the polymerases and the ribosomes. In the final slide fabrication complexity mathematically explains that biology lives at the half max of complexity where a polymer of N monomeric building blocks of Q different types has Q acheiving the half max at 20 for a polymer of length 500. Which is the same number as number of aa (20) for an average human protein size (500). So nature lives right at this balance of complexity and redundancy.

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently?

Answer: Solid-phase phospohoramidite DNA synthesis invented in 1981 by Caruthers which happens one neuclotide at a time in the 3’ to 5’ direction reverse of how nature (DNA polymerase) does it. Dr Prosts slides and Prof Jacobsons slides show some cool mechanism of removing something called the DMT group in certain neuclotide and then flooding the system with neucleiotides and thus synthesizing many parallel oligos at once. Quite remorkable . While the chemistry is the same as in 1981 its is now scaled using silicon-based microchips (like those used by Twist Bioscience) to synthesize upto 1 Million unique oligos at once much more efficiently, significantly reducing associated energy consumption and costs.

2. Why is it difficult to make oligos longer than 200nt via direct synthesis?

Answer: In Phosphoramidite synthesis small inefficiencies compound so with the biological error correction mechanism even industry standard coupling efficiency of 99% becomes catastrophic over hundreds of cycles so even a 200-mer has a overall yield of $0.99^{200}$ which is approximately 13% accuracy. Plus it probably becomes very messy to control because I would assume that the momomers would wanna fold incorrectly.

3. Why can’t you make a 2000bp gene via direct oligo synthesis?

Answer: Error rates would lead to multiple mutations per molecule at that scale leading to very few correct full length sequences and will also take a long time. Thus the slides recommend assembling of smaller gene fragments (around 5kb according to the slides) to reduce error and increase control.

Homework Question from George Church:

1. What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

Answer: The 10 essential amino acids (once we cant produce ourselves) include 9 core EAAs namely Histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine. Arginine is considered essential for specific species. I find it intreseting that life didnt progrzm itself to be fully self sufficient by producing them all. But then maybe thats a higher expenditure on the cellular machinery. Maybe biology evolved to produce the things only if it wasnt abundant in the environment. The Jurrassic park reference of the lysine contingency seems ridiculous since lysine is one of the 10 amino acids animals dont produce themselves sufficiently yet we are able to get them from eating meat and plants. Thus you couldnt keep the dinasours from surviving without suppliying them with lysine because just like us they had the option of getting lysine from eating other herbivores and plants or the Tourists. The slide also shows NSAA (Non-standard amino acids) that can pave the path for us to design synthetic life dependent upon different sets of amino acids. How would that change things I wonder!

Chat GPT 5.2 generated image with prompt: “Futuristic medical illustration of a smart biopolymer patch…”

*Thanks for reading!*

Week 2 HW :DNA Read, Write and Edit

Part 1: Benchling & In-silico Gel Art

• Make a free account at benchling.com
• Import the Lambda DNA.
• Simulate Restriction Enzyme Digestion
• Design using Ronan’s website: Cactus Digest. I found this tool very useful. For the longest time I was just trying to get a good pattern randomly. Then i realised I can edit individual columns. With some more digging it became clear that I need to find restriction enzymes that slice the DNA in the bands that appear like something on the digest. So for the lane 5 I used EcoRV and BanHI to get the nice trunk of the Cactus design. For lane 4 and 6 i used SacI since it only makes two bands. And for lane 3 and & I used EcoRI and BamHI to make the 2 stems of the cactus. A little throwback to where I am from (Rajasthan).

• Create a pattern/image in Benchling- I then recreated ronans website design by performing the digest on the lamda phage genome downloaded from GenBank in Benchling using the digest enzymes that ronans website described. The following is a screenshot of the Virtual Digest in Benchling

Part 2: No remote lab access yet! 🤞

Part 3: DNA Design Challenge

Benchling Folder

3.1 Protein Choice:

Since I am working on building a smart hydrogel I did some literature review and found that Elastin Like Polypeptides (ELPs) are good candidates. ELPs are recombinant protein polymers inspired by the natural protein Elastin, found in the extra cellular matrix, which is responsible for the flexibility of our bodies. ELPs are thermally responsive biopolymers and capable of having customizable properties. ELPs undergo something called an inverse temperature transition. This means that when heated, normal polymers dissolve but ELPs do the opposite. So ELPs stay soluble below a certain temperature and above a transition temperature (Tt) they undergo hydrophobic collapse and aggregate. ELPs interestingly are just repeat pentapeptides with amino acidv(AA) sequence Val-Pro-Gly-X-Gly (VPGXG)n where X can be any canonical amino acid except proline. This interchangebaility of X gives ELPs their customizability and such engineered ELPs exhibit excellent potential as customizable platforms for drug delivery and tissue repair.

structure of ELPs and its impact on its own Tt (Guo Y, Liu S, Jing D, Liu N, Luo X. The construction of elastin-like polypeptides and their applications in drug delivery system and tissue repair. J Nanobiotechnology. 2023 Nov 11;21(1):418. doi: 10.1186/s12951-023-02184-8. PMID: 37951928; PMCID: PMC10638729.)

For this weeks assignment I designed a short ELP consisting of only 10 repeats with X= Serine to creat a moderately hydrophilic scaffold:. So the AA sequence will be: VPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSG

3.2 Reverse Translate: Protein (AA) sequence to DNA (NA) sequence:

For this part I used reverse translation tool online for this as suggested in the homework instructions and got this as a result “>reverse translation of Untitled to a 150 base sequence of most likely codons. gtgccgggcagcggcgtgccgggcagcggcgtgccgggcagcggcgtgccgggcagcggc gtgccgggcagcggcgtgccgggcagcggcgtgccgggcagcggcgtgccgggcagcggc gtgccgggcagcggcgtgccgggcagcggc

3.3 Codon optimization:

• Why optimize?

Now the problem here is that its a highly repetitive DNA with repeats of GTG CCG GGC AGC GGC repeated 10 times. Its correct biologically but as mentioned in Dr Prousts lecture this is what you want to avoid for synthesis stability. So we need to figure out a way to make the DNA sequence less repetative eventhough the AA are repetative. We can do this because each amino acid has more than one codon that can code for it. So lets find the options for each of our AAs. Here they are:

• Valine: GTT, GTC, GTA, GTG
• Proline: CCT, CCC, CCA, CCG
• Glycine: GGT, GGC, GGA, GGG
• Serine: AGT, AGC, TCT, TCC, TCA, TCG

Now we need to find a way to use these options to create a new DNA sequence thats less repetative and more synthesis friendly.

• Codon optimization on benchling

  On Benchling I uploaded the 10 repeats of my AA sequence (VPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSGVPGSG). Then selecting the whole sequence choose back translate. Choosing E.Coli (O157:H7 EDL933). Then Match codon usage to target organism method.

The output of the codon optimization gave the following optimization preview results:

The GC content (66 %) is slightly higher than the E.Coli average (around 50%) but great considering our first DNA sequence was 87 % !

So this is our final optimized 150 bp sequence. It translates to VPGSG 10 times. It has clearly become less repetative.

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.

• Production Technology: Ideally, I would like to produce this ELP using a cell-free protein synthesis (CFPS) system. Cell-free system will allow me to rapidly prototype different ELP lengths without the time-consuming steps of cloning and cell-culture. However, since I am learning the foundational workflows of synthetic biology and tools, I have chosen to codon optimize and design the sequence in E.Coli.

• Why this works: Since the common cell free systems (such as TX-TL) are actually made from e.coli extracts, optimizing the codon for e.coli is a robust strategy that will allow this DNA to be used in both living cells and cell-free synthesis.

• Mechanism (Central Dogma): To produce the protein the DNA is transcribed by RNA Polymerase (recognizing the J23106 constitutive promoter in my design in Part5 below) into messenger RNA. This single stranded mRNA will then be recognized by the ribozomes present in e.coli at the ribozome binding site and thus translating the codon-optimized sequence we made earlier. The ribozome will read the codons three neucleotides at a time and then recruit tRNA with matching amino acids like Valine-Proline etc (that we want and we optimized in benchling for e.coli so they have these tRNAs present and there is no rare codons) and links them together until reaching a stop codon in the mRNA (the T1 terminator) and releasing the ELP polymer. Quite magical how this is identical in all living cells.

3.5 How does it work in nature/biological systems?

1. Describe how a single gene codes for multiple proteins at the transcriptional level.

• Despite the universality there are important variations between various organism to control this process of DNA code becoming protein. In eukaryotes A single gene can code for multiple proteins primarily through Alternative Splicing. During this process, the pre-mRNA (containing both coding and non-coding regions) is produced from transcription. Then something called a spliceosome splices the introns and stitches together the exons. The alteration crucially comes from the spliceosome which doesn’t always stitch together in the same order. It can skip certain exons or include others depending on the cells need. This results in distinct mRNA transcripts from the same DNA template which are then translated to unique protein isoforms with different structures and thus functions.

This is what it would look like to have a gene that codes for our ELP polymer with introns and exons in Eukaryotes.

• Prokaryotes like E.coli do not use splicing, they often use Polycistronic mRNA (Operons). In this system, a single promoter drives the transcription of multiple distinct genes arranged in a row. The resulting long mRNA strand contains multiple Ribosome Binding Sites (RBS), allowing ribosomes to translate each gene into a seperate protein independently. This allows bacteria to turn on entire metabolic pathways with a single switch, rather than cutting and pasting RNA like eukaryotes do.

  I personally am fascinated with the eukaryotic spliceosome and wonder what it would be like to engineer something as complicated and sofisticated.

2. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!!

   

Part 4: Prepare a Twist DNA Synthesis Order

• 4.1: Create Twist and Benchling account

• 4.2: Build your DNA Insert Sequence

  Using the varius parts of the expression casette and the optimized codon we get this (https://benchling.com/s/seq-hjqR0qesr21GIZrfs2Ua?m=slm-glVH1Ltmva9AxlUc2JFg)

  

• 4.3 - 4.6: Final Plasmid from Twist with our ELP insert.

  

• Also our complexity score in twist came out to be standard that means our codon optimization worked and the plasmid is constructable even with our initial repeat codon sequence with high GC content.

  

Part 5: DNA Read/Write/Edit:

5.1 DNA Read:

• (i) What to sequence:

I would like to read the DNA of a Beaver’s gut microbiome and see how it manages to survive eating just trees. I love going camping in the beautiful parks of Ontario such as Algonquin park and always see tons of beavers on my trips. And I am fascinated at the uniqueness of beavers and how they are really architects of their environment much like us humans. And a big part of that is their unique microbiome that lets them survive on something so abundant (trees) and their amazing skills to live under water in the winters. ALso their abilities to hibernate and how thats beneficial to them. Sequencing them could reveal novel enzymes for degrading tough plant materials, or even traits that support their survival under ice during canadian winters.

• (ii) DNA Sequencing approach:

Since the beaver gut probably contains thousands of unknown bacteria and fungi working together to break down wood, I would need to sequence the entire soup of DNA. So Metagenomic sequencing would be a good approach using the Oxford Nanopore Tech (ONT). Nanopore sequencers like MiniON are portable which would be ideal to take with me to algonquin park and sample fresh sample from the field!

• This is very third generation. To be more precise it is Single Molecule Real-TIme (SMRT) sequencing. Since Nanopore unlike 2nd gen methods (that required PCR amplification) can read native, single molecule DNA directly in real time without amplification. This allows for extremely long reads critical for assembling complex metagenomes.

• Wet Steps I will need a field ready extraction kit like once from Zymo Research to lyse the hardy fungal/bacterial cells. I will also need to ligate motor proteins and sequencing adapters to the ends of DNA fragments which will lead the DNA into the nanopore.

• How it Decodes The Nanopore is a protein pore through which current is passed and each combination of neucleotides in the DNA inside the pore creates a unique squiggle that Neural Netorks translate into a nucelotide seq.

• Output Primary output is usually a FASTQ file which is the sequence data with quality scores. For the gut soup project we will have to take the collection of millions of reads and compare it with the Metagenome-Assembeled Genomes to identify our wood-degrading microbes of interest.

5.2 DNA Write:

(i) What to synthesize: Thinking about the DermLogic concept all week I think beyond simple ELPs, I would like to synthesize a “Living Bandage” gene circuit. This could be a genetic circuit designed using skin bacteria like S.epidermis that can sense inflammation markers and respond by synthesizing and secreting healing hydrogel matrix directly into the wound.

(ii) I like the concept of De Novo DNA synthesis using polymerase nucleotide conjugates shared by Prof Joe Jacobson. Its a fast and non-toxic compared to the traditional Phosphoramidite Chemistry. Also its capable of long fragment synthesis which is the future of potentially synthesizing several kilobases without need for extensive assembly. But for the gene fragments ad vectors needed for this type of live bandage I would use the Oligo synthesis using Phosphoramidite chemisry used by companies like Twist Biosciences.

5.3 DNA Edit:

(i) What to edit: I would like to edit the connective tissue cells (fibroblasts) in older adults to restore their ability to produce functional elastin. Since reading about the role of elastin in the human body I was wondering if we could somehow mimic the natural elastin in the extra cellular matrix of patients with lack of elastin causing various dieseases and locally edit the ECM to reverse some of the elastin related aging in older adults. Such ELPs could someday help an old person heal their weak broken bones that are still “fixed” by doctors using screws and foreign metal parts and instead hope for a future where we could perhaps edit the local stem cells to secrete high performance ELPs that regenerate the tissue naturally, restoring the elasticity and strength.

(ii) Technologies

• Design: Since we are dealing with humans we wouldnt want to use standard CRISPR-Cas9 to cut ds DNA but rather use something advanced in CRISPR technologies like Prime editing. It uses a nickase (a broken Cas9 that can only cut one strand) fused to a reverse transcriptase to find a specific site using pegRNA (guide RNA for the system) in the fibroblast genome and directly write a corrected sequence for elastin without causing any dangerous ds breaks.

• Inputs: Firstly we will need the fusion protein delivered as a plasmid or mRNA encoding the nCas-9RT fusion. Thenn we will make the custom guide pegRNA containing the target spacer (DNA site in the fibroblast genome for elastin). Lipid nanoparticle delivery system can be useful for delivery into the fibroblast cell.

• Limitations: Its less efficient (20-50% successful cells edited) than standard CRISPR-Cas9 (80%) Designing pegRNA is harder than standard gRNA

*Thanks for reading!*

Week 3 HW: Lab Automation

Part 1: Python Script Opentron Artwork

Abhishek Udawat Copy of HTGAA26 Opentrons Colab

Fluorescent TukTuk

Part 2: Post-Lab Questions

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

• Since my interest in cell-free systems and biosensors, I found a relevant paper titled “Semi-automated Production of Cell-free Biosensors”

• The Problem: Manually pipetting cell-free reactions is slow, inconsistent across different researchers, and difficult to scale for field use

• Their Solution: The Authors used the Opentrons OT-2 to automate the assembly of flouride riboswitch biosensors

• The Novelty: They didn’t just automate the liquid moving, they optimized the robot’s physical parameters (like blowout height and dispense speed) to handle the viscosity of cell-free extracts. This allowed them to produce 384 consistent, functional sensors in 30 minutes- something that would take a researcher much longer and with higher risk of error.

• The Result: They proved that low-cost robotics can produce “shelf-stable” diagnostics that perform nearly as well as those made by expert humans, making it possible to manufacture sensors for environmental toxins anywhere in the world.

Question 2: What do you intend to do with automation tools for your final project?

• Project Context: My project, DermLogic, invovles creating a smart biopolymer hydrogel patch that utilizes cell-free genetic circuits to detect and treat cutaneous HPV.

• Automation Plan: I intend to use the Opentons OT-2 to standardize and scale the production of these “Living” patches. I envision that the primary challenge with DermLogic will be ensuring the precise ratio of cell-free extracts, DNA logic gates, and biopolymer precursors (hydrogel) to maintain therapeutic consistency.
  Here are some lessons taken from the above paper:

1. Viscosity-Optimized Liquid Handling: Following the methodology in Brown et al. (2024), I will program specific robotic parameters to handle the high viscosity of the hydrogel-extract mixture. This includes tuning the aspirate/dispense rates and implementing touch-tip/blowout sequences to prevent the “bubbling” that often occurs during manual pipetting of cell-free systems.

2. High-Throughput Logic Testing: I will use automation to screen an array of genetic circuits designed to detect HPV oncoproteins. The robot will facilitate a “Master Mix” approach, where the base cell-free machinery is distributed across 96-well plate, followed by the addition of unique DNA constructs. This allows me to test hundreds of logic-gate variants in a single run to see which provides the sharpest “ON” trigger in the presence of HPV markers.

3. Automated Lyophilization Prep: To meet governance goals of a “cell-free mandate” for safety proposed in week 1’s assignment, i will use the Opentrons to dispense the final reactions into patch molds before they are frozen and lyophilized. Automation ensures that every patch has a uniform concentration of the therapeutic peptide, which is critical for clinical safety and efficacy.

• Pseudocode for DermLogic Patch Assembly:

for well in patch_molds:
    p20.pick_up_tip()
    p20.aspirate(10, hydrogel_master_mix, rate=0.5) # Slow aspirate for viscosity
    p20.dispense(10, well, rate=0.5)
    p20.touch_tip(v_offset=-2) # Ensure clean release from tip
    p20.drop_tip()

Final Project Ideas Slide

Week 4 HW: Protein Design- Part 1

Part A: Conceptual Questions

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)

Meat isn’t 100% protein; it’s typically composed of ~20% protein, ~20% fat, and ~60% water. So 500g of meat will approximately contain 100g of protein. Since Daltons is the unit used to measure the weight of small molecules and 1 Dalton is approximately $1.66 \times 10^{-24}$ grams. Since we need to calculate the number of molecules of AAs in our meat, let’s first convert the mass of one AA into grams

$$100\text{ Da} \times (1.66 \times 10^{-24}\text{ g/Da}) = 1.66 \times 10^{-22}\text{ g}$$

Then we divide the total protein mass by the mass per AA

$$\frac{100\text{ g}}{1.66 \times 10^{-22}\text{ g/molecule}} \approx 6.02 \times 10^{23}\text{ molecules}$$

So the total number of AA molecules in 500 g of meat is approximately $6.02 \times 10^{23}$, which is coincidentally (and elegantly) about 1 Mole of amino acids.

• Thus we learn the trick that chemists use when going from molecular weights to weighing scale weights: If a molecule weighs 1 Da then 1 mole of it will weigh 1 gram. In this case since AAs weigh 100 Daltons, 1 Mole of AAs will weigh 100 grams (which is also the amount of protein in 500 grams of meat). Mind is BLOWN!

2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?

• Humans dont become cows because we dont incorporate bovine proteins directly. Through digestion, we break down those proteins down into their constituent amino acids. Since the 20 natural amino acids are a universal biological language, our body simply uses them as raw materials to build new proteins based on human DNA sequences.

3. Why are there only 20 natural amino acids?

• There is a theory proposed by Crick that this is a frozen accident; Once life settled on 20, the cost of changing the entire genetic code was too high.
• The other theory says that its the Optimal set; these 20 provide enough variety (acidic, basic, polar, non-polar) to build amost any functional shape. Adding more might have had diminishing returns or caused too many “side reactions”
• As mentioned in the last slide of Joe Jacobsons lecture 20 provides the optimal balance of codon redundancy and diversity.

4. Can you make other non-natural amino acids? Design some new amino acids.

• Every amino acid has the same basic structure: an amino group, a carboxyl group and a side chain (R-group). To design as new one, you typically keep the backbone the same so the ribosomes can still physically link it, but you can change the R-group to do something nature can’t. So lets design a “Metal-Sensing Amino Acid” since I am interested in how metals can be bound to proteins. For this we can use Bipyridine as our modified R-group because it loves to grab onto metal ions. And we can use UAG (stop codon) to code for our new AA. I found this paper titled “Rewiring Protein Synthesis: From Natural to Synthetic Amino Acids” and in it they describe that we need to modify two specific biological parts to make our synthetic AA. Firstly we need to evolve the Aminoacyl-tRNA Synthetase that are essential enzymes that catalyse the attachment of a specific amino acid to its corresponding tRNA. We can take a natural one and mutate its active site to fit our Bipyridine so it ignores the other 20 natural AA. The resulting tRNA is delivered to the ribosome by an elongation factor which we will have to modify to deliver our bulky new AA.

5. Where did amino acids come from before enzymes that make them, and before life started?

• Before enzymes existed, chemistry had to synthesise amino acids through abiotic chemistry fueled by the geological and cosmic energy. The main theories of our time come from:
  • Miller-Urey Primordial Soup: This theory suggests that the early atmosphere of earth was a reducing environment rich in methane, ammonia and water vapor. Miller and Urey found that passing electrical sparks through these gases produced several amino acids including glycine and alanine
  • Deep-Sea Hydrothermal vents: As we remember watching deep sea thermal vents footage from BBC’s planet earth documentary. These cents provide a constant stream of superheated, mineral rich water and reducing gases like H2 and H2S which provides extreme pressure and temperature gradients which combined with the mineral catalysts could drive the synthesis of organic molecules. This is refferred to as the iron-sulfur world theory.
  • Panspermia: One of my favorite theories. Amino acids may not have started on earth at all but were delivered by comets and meteorites. Like a message of life from beyond. Who wrote it? How long ago? Haha. I am gonna write a sci-fi story one day with this premise. The evidence for this theory came from Murchison meteorite which fell in Australia in 1969 and was found to contain over 70 different amino acids.

6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

• This question somehow scares me. Since this relates to Chirality in life. And how fascinatingly all life on this planet is formed by L-amino acids. Contemporary notion is that if we mess around with this and make life from the mirror image which is D-amino acids then we will form helices with a mirrored spin and life that will be incompatible with the enzymes of L-AA oriented life and how catastrophic something like this will be in the wild. But I hold an optimists approach. I feel like this is going to be a huge area of research in synbio and we should approach this problem with good ethics and positivity.

Here is the correct difference between L-D Amino acids.

Here is the hallucinating ChatGPT image that cant draw a correct Amino Acid for the life of me. I have tried many times. But they say its gonna take over the world. It got some parts right. prompt: ChatGPT please draw me an image showing L and D aminoacids showing chirality of life and how they form mirror alpha helices

• PyMOL chirality: Later used pymol to show L and D Alanine side by side

7. Can you discover additional helices in proteins?

• Yes, additional helical structures besides the standard alpha delix can be found in proteins. Studies show that other types of helices occus in many proteins, but they are often overlooked or mistaken for small distortions in alpha helices. These helices are especially common in memberane proteins and are found in a significant number of known protein structures. (Vieira-Pires & Morais-Cabral, 2010)

8 Why are most molecular helices right-handed?

• As shown above they are mostly righ-handed because there is more abundance of L-Amino acids. So when coil righ handed helices avoid steric clashes and are more stable and thus more common

9. Why do β-sheets tend to aggregate? What is the driving force for β-sheet aggregation?(also answers Q11 )

• The primary sequences of beta strand and sheet prefer alternating hydrophobic and hydrophilic amino acids. This forms exposed hydrogen bond donors and acceptors on both sides that forms the sheet structure and the hydrophobic side chains interlock between the sheets. This is shown in the image below from pyMol

  You can shee in the above image that Hydrogen bonds in grey small dots create horizontal structure and Hydrophobic packing on the inside denoted by the orange surface creates the vertical stacking.

Part B: Protein Analysis and Visualization:

1. Protein Selection and Rationale

• Protein: I selected 4IS4, the Glutamine Synthetase from the model plant Medicago truncatula. I chose this because GS is the primary engine for nitrogen assimilation in plants. By studying a plant-specific decameric structure, I can better understand how to engineer nitrogen uptake in duckweed—a key goal for my PACT proposal to reduce agricultural fertilizer runoff in Canada.
• Rationale: GS is the “gatekeeper” for nitrogen assimilation. It converts ammonium (found in high concentration in Canadian hog/dairy manure) into amino acids.
• The Goal: To see of we can engineer a version of this protein that works faster or remains stable in the fluctuating temperatures of a canadian lagoon.

2. Amino Acid Sequence:

The length of the protein is: 378 aminoacids. The most common amino acid is: G, which appears 42 times.

• Sequence Homologs: There are thousands of homologs of the protein as this is a highly conserved protein across species.

4. The structure was released on 2014-04-09. The resolution is 2.35 Å so its a very good resolution.

Interactive Model: View the decameric structure of 4IS4 on RCSB PDB

4. 3D molecule visualization software images:

Cartoon Image- front: Cartoon Image- side: Ribbon Image- Front: Most- Informative:

• Color by secondary structure:

• seems like a pretty equal representation of sheets and helices which is a common feature in enzymes

• Color by Residue Type: Hydrophobic: Orange; Hydrophilic: Blue

• Since this is an enzyme it has tons of pockets

Part C. Using ML-Based Protein Design Tools

• C1. Protein Language Modeling

  a. Unsupervised Deep Mutational Scans

A mutation scan is basically asking “If I mutate every position to every other amino acid, which ones does the model think are tolerated… and which ones are catastrophic?” In the heatmap the X-axis represents the AA position in the protein (1-378). And the Y-axis represents the 20 possible amino acids.

• Deep Blue/ Purple Columns: These are “Cold” regions. they represent positions where almost any mutation results in a significantly lower model score. This indicates High Conservation. In glutamine synthetase these are usually residues critical for binding ATP, Glutamate or forming the decameric ring interface.
• Yellow/Green Spots: These are “Neutral” or “Favorable” regions. The model suggests the protein could tolerate these changes without losing its structural integrity.

• Mutation that Stands out: I observed a distinct pattern of high sensitivity around Position 2-12 which probably is the catalytic site of the enzyme.

TBD: (Bonus) Find sequences for which we have experimental scans, and compare the prediction of the language model to experiment.

b Latent Space Analysis

The Latent Space analysis was cool to visualise but the neighbourhood doesnt give anything useful unfortunately.

C2. Protein Folding

I got a pyMol structure of a monomer of my protein and compared it to the ESM Fold predicted structure and I have to say these snapshots show a lot of similarities of you focus on the number of parallel B-sheets and their location

• 
• 

• Confidence score: Mostly Blue so ESM has a high confidence in this structure which it should

C3. Protein Generation

Inverse folding using protein MPNN

Following were 4 generated sequences along with the 1st being the native sequence

These generated sequences closely resemble our original structure so ProteinMPNN did a great job at finding various sets of sequences giving the same PDB structure we entered.

Generated sequences…

4IS4, score=1.5294, fixed_chains=[], designed_chains=['A'], model_name=v_48_020 MSLLSDLINLNLSESSEKIIAEYIWVGGSGMDLRSKARTLPGPVSDPSKLPKWNYDGSSTNQAPGQDSEVILYPQAIFKDPFRQGNNILVICDVYTPAGEPLPTNKRYNAAKIFSHPDVAAEVPWYGIEQEYTLLQKDTNWPLGWPIGGYPGPQGPYYCGIGADKAYGRDIVDAHYKACLYAGINISGINGEVMPGQWEFQVGPSVGISAGDEIWAARYILERITEIAGVVVSFDPKPIPGDWNGAGAHTNYSTKSMRENGGYEIIKKAIEKLGLRHKEHIAAYXXXXXXXXXXXXXXXXXNTFSWGVANRGASVRVGRDTEKDGKGYFEDRRPSSNMDPYVVTSMIAETTLLWKP

T=0.1, sample=0, score=0.8142, seq_recovery=0.4985 EELKELLENIDLSLLTTKIIATYVWIGGDGTTLKSFSVELPGPITNPADLPLTSVDGSKYNLSSGADTTVILVPRKIFRDPFLKGDNIIVVCSLKTPSGEPLPTDTYAKAAEIFAHPKVAAEQPEFTITIEFTLLDKETGLPLGYPPEGYPGPDNPYRNGTGPDTAFGQEILDETVEACKYAGIPISGSAREVEPGQWSITVGPALGIDAGLHVVAARYILEKIAEKAGVKVSFKDKPYPGPYYSKGAPVEFATKAMRGEGGLALILEAIEKLKKRYSELLAAVXXXXXXXXXXXXXXXXXATPSWSVDTPGSSFQIGAATVEAGRGSFTFLVPKSDADPFIVTGMLAEITLLYED

T=0.1, sample=0, score=0.8052, seq_recovery=0.5074 AAAAAALANVDRSLLTTKIKAEYVFIGGDGTTLRSFSVELPGPITDAKDLPLSSFDGSKYNMASGSNTTVILKPEKIFKDPFLGGDNIIVVCSSYTPEGKPLPTDKYAEAKEIFDKPEVKAEQPQFTIRIEFTLLDAETGLPLGYPASGYPGPNGPWRNGTGPNTAFGMDIVNETVAACKYAGIPISGSASEIEPGQWSITVGPALGINAGLNVIAARYILEKIAAKAGVKVSFDDKPVPGPYYSRGAPVGFSTKTMRGAGGLAAILAAIEKLGLRYEELKKAFXXXXXXXXXXXXXXXXXSEFSWSVDTEGSSFRIGAATVAAGKGSFTVLVPPSNANPFVVTSMIAYYTLLYKP

T=0.1, sample=0, score=0.8224, seq_recovery=0.5192 AEALAALENIDLSLLTTKIIAEYVWVGGDGTTLRSFSVELDGPITDPSKLPLTSVDGSKYGLASGPDTTVILVPEAIFKDPFRKGNNIIVVCSLRTPSGEPLPTDKLAEAREIFDKPEVKAQQPEFTVRIEFTLLDKETGKPLGYPPEGYPGPNNPYYNGTGPDTAFGDEIVNEAVEAMKYAGIPISGSEREIEPGQWSYTVGPALGIQAALNVIAARYILEKIAEKHGVNVSFKPKPVPGPWYSRGMPVGYATKAMRGPGGLAKILADIEKLAKRYPELLAAFXXXXXXXXXXXXXXXXXATFSWSVDTPGDSFRIGAETVAAGRGSFTVLLPPSDADPAVVTGMLARVTLLEKP

T=0.1, sample=0, score=0.8041, seq_recovery=0.5074 AAEELAKKNVDRSLLTTEITAEYVFIGGDGRTLKSFSVTLPGPVTNPADLPLTSFDGSKYNMASGPDTTVILKPKKIFKDPFLKGNNIIVICSTYTPAGTPLPNNKYAEAEAIFADPAVAAAQPLFTIEIEFTLLDADTGLPLGYPADGYPGPVNGWENGTGPDTAFGKDILDKTVEACKYAGIPISGSAREVEPGQWSFTVGPALGIEAGYHVLAARYILEKIAEEAGVKVSFAPRPVPGPWYTRGAPVSFATAAMRGPGGLAVIDAAIARLAARYPELLAAVXXXXXXXXXXXXXXXXXATPSWSVDTEGASFRVGADTVAAGRGSFTFLVPRSDANPFVVTSLLARVTLLDTP

Part D. Group Brainstorm on Bacteriophage Engineering:

• Make a group: Tammy Sisodiya (London Node); we are looking for collaboration
• Engineering Goal:
  • Start with making L-Protein more robust
• Computational tools:
  • ESM-2 to perform deep mutational scan to find which mutations the model thinks will improve stability
  • Protein MPNN to redesign the sequence around the core structure to find more optimal amino acid arrangements
  • ESMFold to check if new sequence still folds correctly
• Proposal:
  • Sequence ——> ESM2 Scan ——> ProteinMPNN ——–> Redesign ——> ESMFold Validation
  • I chose ESMFold because its faster at testing multiple mutation iterations in silico
  • ML Models have limitations; they are great at folding but might not perfectly predict how a protein behaves inside a complex living cytoplasm.
• Reading:

  • MS2 Lysis of Escherichia coli Depends on Host Chaperone DnaJ:

  • Mutational analysis of the MS2 lysis protein L

*Thanks for reading!*

Week 5 HW: Protein Design- Part 2

Part A: SOD1 Binder Peptide Design (From Pranam)

Part 1: Generate Binders with PepMLM:

1. Human SOD1 AA sequence from UniProt (P00441):

MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

• PyMOL image with A @4 is highlighted

• Interesting find: I didnt know that all protein synthesis starts with amino acid Methionine(Met) which is cleaved off by Methionine Aminopeptidases as an essential process in a large majority of cells. It also has a condition where only the sequences with these small uncharged AAs as a second AA can be functionally folded instead of being degraded. (Nguyen et.al.2019)

• Introducing SOD1 Seq with A4V Mutation (removing Alanine and adding Valine) associated with the most aggressive form of the ALS disease (and removing the initial methionine) : ATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

2. PepMLM Generation Results

Note: Lower perplexity indicates higher model confidence in the sequence’s fit for the SOD1 mutant.

Part 2: Evaluate Binders with AlphaFold3:

AlphaFold3 Evaluation Table

1. Design_1 (WRSPATGARHKK) + SOD1:

2. Design_2 (WRVPAVAVRHKK) + SOD1:

3. Design_3 (HRYPVVGAEWKK) + SOD1:

4. Design_4 (WRYYAAAIAHKK) + SOD1:

5. Control_Peptide (FLYRWLPSRRGG) + SOD1

At this point I realised that I was only doing the pepMLM and alpha fold with the monomer of the protein so the binder I am trying to design is for the enzymes natural dimer state, So I am going to redo the above sections and see if I get different scores.

2. PepMLM Generation Results for Dimer and 15 AA Peptide length (first 4 designs):

Note: Lower perplexity indicates higher model confidence in the sequence’s fit for the SOD1 mutant.

Part 2: Evaluate Binders with AlphaFold3:

AlphaFold3 Evaluation Table

1. Design_1 (WTWVVHVATHKHHLK) + SOD1:

2. Design_2 (DTVVHHHATHEHKKK) + SOD1:

3. Design_3 (WTVEHHLVTKQEKKK) + SOD1:

You can see the peptide burried in the active site for the first time and both ends closest to the A4V Mutation sites of both monomers of the enzyme

4. Design_4 (DTWDHHLATKEHKKK) + SOD1:

5. Design_5 (WTRVHAVVEKKK) + SOD1:

6. Design_6 (ATHVHVAIHHKK) + SOD1:

burried peptide

Control_Peptide (FLYRWLPSRRGG) + SOD1

PepMLM and AlphaFold Results Interpretation:

1. Significant improvement in Confidence (ipTM): Dimer breakthrough: Switching to a homodimer target in PepMLM was the turning point for better AplphaFold scores.
2. Perplexity vs structure correlation: Visually the best peptides for the dimer were Design 3 (WTVEHHLVTKQEKKK), Design 6 (ATHVHVAIHHKK) and Design 4 (DTWDHHLATKEHKKK). Interestingly these also had the best perplexity score (10.65, 7.73 and 10.52 respectively) but not necessarily the best TM scores.
3. The Design 6 peptide with the dimer (ATHVHVAIHHKK) got the highest ipTM (0.88) and pTM (0.91) scores closest to the control (FLYRWLPSRRGG) which got the same ipTM (0.88) and pTM (0.91) scores. The control didnt get a good perplexity scores and interestingly the control peptide and several designs seem to be on the same spot not near the N-terminus but near one side of the b-sheets. . Both scores dont always seem to align. It seems that perplexity measures how natural the sequence feels to the language model, but ipTM measures the physical docking.
4. Targeting the A4V Mutation: While the Control Peptide and designs showed high general binding scores, Design 6 and 3 were the only ones that were localized near the N-terminus, which is the actual site of the A4V mutation. This suggests that raw scoring (ipTM) doesn’t always guarantee site-specific binding. Alpha fold likes the control peptide, pepMLM doesnt.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

After comparing all the results seems to me that the best binders (binder 3 and 6) with respect to the perplexity scores and AlphaFold dont necessarily have the best binding affinity. The best affinity seems to be of Design 5 which actually had a good AlphaFold scores as well (ipTM :0.82) but not far more than the other peptides. They all seem to have good solubility and are non-hemolytic.

Based on all the calculations above I will choose Design 6 (ATHVHVAIHHKK) with the best perplexity score, best AlphaFold score and good therapeutic properties.

Part 4: Generate Optimized Peptides with moPPIt:

All three peptide binder designs from moPPIt-v3 just knocked the binder design out of the park compared to the PepMLM binders. In no time it created stable binders at the right site (as confirmed with alphafold results below) with great affinity (highest @ 9.4 vs 4.8 from our best design above). All three are very soluble, non-hemolytic and decent binding to our target motif residues in the SOD1 protein (I chose motif residues 2-6 and 143-154).

Design 1:

Design 2:

Design 3:

Part C: Final Project: L-Protein Mutants

• Objective: Improve the stability and auto-folding of the lysis protein of a MS2-phage

• Wild-Type L-protein: Here is what we are working with

• Key Summary of findings: The mutational analysis of MS2 L identifies the highly conserved LS motif (Leu 48 - Serine49) as the functional “trigger” for lysis. Experimental data also shows that even trace amounts of functional protein can cause lysis, but the protein is currently limited by its dependency on the DNA J host chaperone which the bacteria uses by P330Q mutation to get resistant(Chamakura et al., 2017).

De Novo L-Protein Strategy:

To bypass the DNAJ dependency, I am using moPPIt to redesign the L-protein as a self-folding antimicrobial agent.

1. Targeting the N-terminus: I am replacing the disordered, basic N-terminal domain (residues 1-35) with a stable, de novo- designed alpha-helix. This removes the regulatory domain that normally requires DNAj for displacement.

2. Maintaining Lysis activity: I am preserving the conserved hydrophobic C-terminal domain and the LS motif to ensure that the protein retains its ability to integrate into the host membrane and trigger autolysis.

3. Stability optimization: I am using AlphaFold2 to validate that the new sequence achieves a high confidence score in isolation. A high score suggests the protein will fold independently and rapidly, reducing the time window for the host to mount a resistance response.

Tool Pipeline Used:

moPPIt (for multi-objective optimization) ——-> AlphaFold2 (for structural validation of autofolding potential) ——–> PeptiVerse (check for high solubility and binding affinity)

Method:

• target protein to modify on moPPIt : METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLLEAVIRTVTTLQQLLT
• binder length to be generated by moPPIt: 75 (original size of the L-protein)
• Objective and weights in moPPIt:
  • Hemolysis: We choose nontoxicity as score 1 at first eventhough we are creating a lysis protein. We can change this to 0 if we get low affinity.
  • Solubility: We choose 1 because we need good solubility. This will hopefully fix the unstable N-terminal.
  • Affinity: We choose 1 because we are looking for high affinity in the c-terminal portion.
  • Motif: Keep the generated sequence anchored to the functional lysis residues (i.e. transmembrane region: 41-75, LS-Motif: 48-49).

Results:

L-Protein Variants designed my moPPit:

• Variant 1 AlphaFold2 structure:

Interestingly the AlphaFold3 version that we were using for Pranams Assignment (which is using diffusion models instead of geometry transformers in AF2) gives a slighlty better image

• Variant 2:

• Variant 3:

• Variant 4:

• Variant 5:

Discussion:

My lead candidate, Variant 1, was generated with a high Affinity score (9.35) and Motif score (0.68). Validation via AlphaFold3 shows the emergence of beta-sheet secondary structures in the redesigned N-terminus. While the overall pTM score is 0.32, the localized folding in the region previously dependent on DnaJ suggests a more rigid, self-stabilizing scaffold. This supports the strategy of using de novo helical/sheet designs to bypass the need for host chaperones like DnaJ (P330Q). By comparing these variants, it is evident that moPPIt can navigate the trade-offs between solubility (for DnaJ independence) and membrane affinity (for lysis efficiency). Variant 4 emerges as the primary choice for synthesis due to its superior motif preservation and balanced biophysical properties.

Part B: BRD4 Drug Discovery Platform Tutorial (Gabriele):

Hit:

Lead:

Candidate JQ1:

Week 6 — Genetic Circuits Part I: Assembly Technologies

Part A: DNA Assembly:

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

• The Phusion Master Mix is designed for high accuracy and speed. It consists of:
  • Phusion DNA Polymeraase: A pyrococcus like enzyme fused to a processivity-enhancing domain. Its purpose is to catalyze DNA synthesis with extremely high fidelity (50x higher than Taq) and speed.
  • dNTPs (Deoxynucleotide Triphosphates): The building blocks (dATP, dCTP, dGTP, dTTP) used by the polymerase to synthesise the new strand.
  • Reaction Buffer: This maintains the ideal pH and ionic strength. These have been optimised for a long range of AT and GC content and includes MgCl2 (cofactor for the DNA polymerase)

2. What are some factors that determine primer annealing temperature during PCR? The Ta (Annealing temperature) is critical for ensuring primers bind specifically to the target DNA

   • Melting temperature (Tm): Ta is ususally set 3-5 degree C below the Tm
   • Primer length and sequence: Longer primers and those with higher GC content have more hydrogen bonds and require a higher Ta
   • Salt Conc: the concentration of monovalent cations (like K+) and divalent cations like Mg2+ in the bugger stabilizes te DNA duplex affecting the Tm

3. PCR vs Restriction Enzyme Digest

   • Both methods create linear DNA, but thet serve differnt roles:

   Feature	PCR (Polymerase Chain Reaction)	Restriction Enzyme Digest
   Mechanism	De novo synthesis/amplification of a specific region using primers.	Enzymatic “cutting” of existing DNA at specific recognition sites.
   Flexibility	Extremely high; you can amplify any sequence and add “tails” (like Gibson overlaps).	Limited to where specific restriction sites already exist in the DNA.
   Fidelity	Depends on the polymerase; high-fidelity enzymes (Phusion) are needed to avoid mutations.	Very high; you are simply cutting existing, usually verified, DNA.
   When to Use	Best for generating inserts from genomic DNA or adding specific sequences/overlaps to ends.	Best for linearizing a circular plasmid backbone or “snapping” out a known part from a carrier.

4. Ensuring compatibility for gibson assembly To make DNA “Gibson-ready”, we must ensure:

   • Overlapping Ends: Each adjacent fragment must share 15-40 bp of homologous sequence at their ends. This is typically achieved by adding these sequences as “tails” on our PCR primers.
   • Purification: PCR products and digests must be purified using gel extraction or column cleanup to remove the original template DNA, primers and enzymes (like 5’ -> 3’ exonuclease) that could interfere with the reaction.
   • Correct Tm of overlaps: The overlapping regions should ideally have a Tm > 48 C to ensure they anneal properly at the isothermal reaction temperature (50 C)

5. How Plasmid DNA enters E.coli During Heat shock transformation:

   • Cells are fiirst treated with calcium chloride (CaCl2). The Ca2+ ions neutralise the negative charges of both the DNA and the cell membrane, allowing the DNA to move close to the surface.
   • Moving the cells from ice at 0C to 42 C creates a thermal imbalance that increases membrane fluidity and creates temporary pores in the cell membrane.
   • The plasmid is then pulled into the cell through these pores potentially diven by the change un membrane motential and simple diffusion.

6. Alternative method: Golden Gate Assembly

   • Explanation: not as flexible as gibson assembly because it uses restriction enzymes that cut at specific sites.

Pre-lab: Primer Design for gibson assembly:

Acropora millepora chromoprotein (amilCP) variants and Bases:

Primers

1. Universal Forward Primer:

This primer is designed to facilitate the seamless assembly of the genetic construct into the destination vector. Sequence B (5’ -cacatccccctttcgccag -3’) serves as the homology tail derived from the pUC19 plasmid, providing the 19bp overlap required for the gibson exonuclease to “chew back” and join the fragments. Sequence A (5’-gaattcggtctctatatgcaggtg-3’) is the annealing segment that specifically targets the mUAV backbone. This 3’ segment has been optimized for a melting temperature of 55.1 C ensuring high fidelity during the PCR amplification step.

• [Sequence B] + [Sequence A]
• 5’ - [cacatccccctttcgccag] + [gaattcggtctctatatgcaggtg] - 3'

2. Universal Reverse Primer:

• 5’ ctgtggtgataaaatatcccaag 3

3. Color Forward Primer:

• Part A(18-24 bp) before the mutation + Part B (mutation site) + Part C( 18-24 bp after the mutation site )
• Orange: cttgggatattttatcaccacagACTGCTtacggaagcataccattc
• Blue : cttgggatattttatcaccacagCAGTACtacggaagcataccattc
• pINK : cttgggatattttatcaccacagTACTGGtacggaagcataccattc

4. Color Reverse Primer:

5’ - [gtcgggaaacctgtcgtgccag] + [gcagggtctcaatatgcaggtg] - 3’

• mUAV region after terminator: gcagggtctcaatatgcaggtg (Binds to the end of the backbone).
• pUC10 reverse compliment : gtcgggaaacctgtcgtgccag (Homology for the downstream junction)
• • Full Primer Sequence: gtcgggaaacctgtcgtgccaggcagggtctcaatatgcaggtg

Week 7 — Genetic Circuits Part II: Neuromorphic Circuits

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

• Practice NW:

High Pass

Low Pass

Dual Region

1. Advantages of IANNs over Boolean Circuits

Traditional Boolean circuits are limited to binary “True/False” logic, which struggles with the “fuzzy” nature of biological environments. IANNs provide:

• Noise Filtering: By using weighted thresholds, IANNs can ignore transient spikes (like exercise-induced miR-26) that would normally trigger a false positive in a Boolean switch.
• Analog Resolution: They allow for a graded response. In my DermLogic patch, this means the hydrogel can change density proportionally to the severity of the biomarker signal.
• Complex Pattern Recognition: IANNs can integrate multiple inputs (miR-21 AND miR-26) to make a single “calculated” decision, similar to how a neuron fires only when a specific summation of signals is reached.

2. Application: The DermLogic Subtractive Patch

• Input/Output: The inputs are miR-21 (pathology signal) and miR-26 (physical activity noise). The output is the expression of ELP hydrogel.
• Behavior: The IANN performs a subtraction (Output = miR21 - w . miR26). This ensures the patch only assembles/disassembles when the pathological signal outweighs the background noise of the user’s daily movement.
• Limitations: IANNs face “Metabolic Load” limits; running complex neural math in a cell-free system requires high concentrations of Csy4, which can deplete the resources needed for the output protein (ELP).

3. 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. Existing Fungal Materials

Examples include Mycelium-based packaging (e.g., Ecovative) and fungal leather (MycoWorks).

• Advantages: They are carbon-negative, biodegradable, and grow on agricultural waste.
• Disadvantages: They are currently slower to “manufacture” than plastics and can be sensitive to moisture, leading to premature degradation.

2. Genetic Engineering in Fungi

I would want to engineer fungi to secrete specific therapeutic enzymes upon sensing a skin pathogen.

• Why Fungi? Fungi are eukaryotes, meaning they can perform complex “post-translational modifications” (like glycosylation) that bacteria cannot. This makes them better “factories” for human-like proteins.
• Advantage over Bacteria: Fungi possess a robust secretory pathway and can form large, physical structures (mycelial mats) that serve as both the “factory” and the “bandage” simultaneously.

Assignment Part 3: First DNA Twist Order

Project Overview: Skin microRNA Receptor Patch

For this week’s assignment, I integrated my Final Project Aim 1 into the neuromorphic circuit framework. I designed a DNA construct (DL_Final_Integrated_Patch_v1) intended to function as a smart, bio-responsive interface.

1. The Design Logic (Neuromorphic Approach)

The circuit is designed to sense specific skin microRNAs (such as miR-21 or miR-26) which serve as “weighted inputs.” Unlike traditional digital logic (0 or 1), this circuit aims to emulate Intracellular Artificial Neural Networks (IANNs) by:

• Analog Sensing: Responding to varying concentrations of microRNA rather than a simple on/off state.
• Thresholding: Using the genetic architecture to trigger a response only when a specific “signature” or threshold of biomarkers is sensed.
• Material Output: Expressing ELP (Elastin-Like Polypeptide) to modulate the physical properties of the hydrogel matrix.

2. Genetic Architecture & Components

The current construct includes several key components visualized in my design:

• Promoters & RBS: Utilizing parts like J23106 and B0034 to ensure reliable baseline expression.
• Csy4 Processing: Used for RNA transcript maturation or gating to clean up the “noise” in the circuit.
• ELP Matrix: The ELP sequence allows the genetic output to be translated into a structural change in the hydrogel, effectively creating an Engineered Living Material (ELM).

Reflective Note: I am exploring how to move beyond simple Boolean gates. While the design is in its first iteration, the goal is to create a “weighted” response system where the hydrogel’s state is a direct “calculation” of the skin’s molecular environment.

3. DNA Synthesis & Backbone Specifications

In accordance with the Week 7 Assignment Part 3 requirements, this construct is designed for synthesis in a high-efficiency vector optimized for cell-free protein expression.

4. Reflective Note: Beyond Boolean Logic

In traditional synthetic biology, sensors are often designed as simple “ON/OFF” Boolean switches. However, for a sweat-sensing patch, the biological environment is naturally “noisy”—for example, physical exercise can cause a 40-fold spike in miR-26, which would normally trigger a false positive in a standard gate.

By adopting a neuromorphic architecture, I am treating miR-21 (the signal) and miR-26 (the exercise noise) as weighted inputs to a single-layer perceptron. Utilizing the Csy4 endoribonuclease as a subtractive processor allows the circuit to perform a real-time analog calculation (Output = Signal - Noise) directly within the ELP hydrogel matrix. This ensures that the diagnostic output is a true reflection of skin pathology rather than a byproduct of the user’s physical activity.

Week 9 — Cell-Free Systems

Part A: General and Lecture- Specific

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

• Flexibility & Control: Unlike living cells, CFPS is an “open” system. You can directly manipulate the reaction environment—adjusting pH, redox potential, or adding non-natural amino acids—without worrying about maintaining cell viability.
• Speed: It bypasses the time-consuming steps of transformation, cell culture, and scale-up, allowing for results in hours rather than days.
• Beneficial Cases:
  • Toxic Proteins: Expressing proteins that would kill a living host cell (e.g., certain antimicrobial peptides).
  • Rapid Prototyping: Testing many different genetic designs quickly in a high-throughput format.

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

• Cell Extract: Provides the molecular “machinery,” including ribosomes, tRNAs, and initiation/elongation factors.
• Energy Source: Molecules like phosphoenolpyruvate (PEP) or creatine phosphate used to regenerate ATP and GTP.
• Amino Acids: The essential building blocks for synthesizing the protein chain.
• DNA Template: The genetic instructions (plasmid or linear PCR product) for the target protein.
• Buffer/Salts: Maintains optimal pH and ionic strength (especially Mg2+ and K+) required for ribosomal function.

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.

• Criticality: Energy is consumed rapidly during transcription and translation. Without a regeneration system, the reaction stops once the initial ATP pool is depleted, leading to very low yields.
• Method: Use an enzymatic substrate system, such as the Creatine Phosphate/Creatine Kinase system, which transfers a phosphate group back to ADP to regenerate ATP in situ.

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

• Prokaryotic (e.g., E. coli): High yield, fast, and inexpensive. However, it lacks complex post-translational modifications (PTMs).
  • Protein: GFP (Green Fluorescent Protein) for simple reporting or biosensing where complex folding isn’t required.
• Eukaryotic (e.g., CHO or Wheat Germ): Lower yield but capable of complex folding and PTMs like glycosylation.
  • Protein: Human Insulin, which requires specific disulfide bond formation and folding pathways not present in bacteria.

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.

• Challenges: Membrane proteins are hydrophobic and often aggregate or misfold when synthesized in an aqueous cell-free mix without a lipid environment.
• Optimization Strategy: Integrate synthetic lipid bilayers

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.

• Template Degradation: - Reason: Presence of RNases or DNases in the extract.
  • Strategy: Use high-purity DNA and supplement the reaction with RNase inhibitors.
• Energy Depletion: - Reason: Fast consumption of ATP/GTP.
  • Strategy: Increase the concentration of the energy buffer or use a dialysis-based continuous-exchange system.
• Codon Bias: - Reason: The DNA sequence uses codons that are rare in the organism the extract was made from.
  • Strategy: Use a codon-optimized gene sequence or supplement the reaction with a mixture of rare tRNAs.

Homework question from Kate Adamala

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

1. Pick a function and describe it.
   • Function: DermLogic is a dual-channel AND-gate biosensor designed for point-of-care HPV detection and therapeutic antisense RNA delivery.
   • Input/Output: The input is extracellular HPV L1 and E6/E7 RNA sequences; the output is a dual-fluorescence signal (GFP/mCherry) and the synthesis of therapeutic antisense RNA.
2. Could this function be realized by cell-free Tx/Tl alone, without encapsulation?
   • Yes, the core logic functions in a bulk cell-free mix. However, encapsulation is required for the “therapeutic patch” vision to protect the RNA payload from degradation and to concentrate the reagents for faster kinetics.
3. Could this function be realized by genetically modified natural cell?
   • It is difficult. Natural cells have complex innate immune responses (like interferon pathways) that might interfere with or degrade synthetic RNA logic circuits and antisense outputs.
4. Describe the desired outcome of your synthetic cell operation.
   • A low-cost, decentralized tool that stratifies HPV risk and simultaneously produces a customized therapeutic response without a cold chain.

2. Design all components that would need to be part of your synthetic cell.

1. What would be the membrane made of?
   • A robust, shelf-stable lipid bilayer composed of POPC and Cholesterol, or potentially a polymeric/hybrid vesicle for better stability during lyophilization.
2. What would you encapsulate inside?
   • BL21 DE3 Lysate, the pDermLogic-v1 plasmid, T7 RNA Polymerase, amino acids, and an energy regeneration system (PEP/ATP).
3. Which organism your Tx/Tl system will come from?
   • Bacterial (E. coli) is preferred. It is highly efficient for T7-driven transcription and the toehold switches are optimized for bacterial ribosomes.
4. How will your synthetic cell communicate with the environment?
   • Through expressed membrane pores like alpha-hemolysin (aHL). These allow the viral RNA “triggers” to enter the cell and the fluorescent/therapeutic outputs to be detected or released.

3. Experimental details

1. List all lipids and genes.
   • Lipids: POPC, Cholesterol.
   • Genes: sfGFP (Channel A reporter), mCherry (Channel B reporter), and a custom antisense RNA sequence targeting the HPV E6/E7 junction.
2. How will you measure the function of your system?
   • Using a dual-channel fluorescence readout (Spark Plate Reader) to monitor the AND-gate logic, and denaturing PAGE gel electrophoresis to verify the production of the ~21 nt antisense RNA.

Homework question from Peter Nguyen

• Pitch: “Bio-Sensing Textiles: A garment that changes color upon detecting hazardous environmental pathogens.”
• How it works: Freeze-dried cell-free systems containing a specific RNA-based biosensor (riboswitch) are embedded into fabric fibers. Upon exposure to a specific pathogen and moisture (sweat or atmospheric water), the system rehydrates, triggers the sensor, and expresses a chromoprotein that visibly stains the fabric.
• Societal Challenge: This addresses the need for passive, wearable safety monitoring for healthcare workers or soldiers in environments with invisible biological threats.
• Addressing Limitations: The “one-time use” nature is addressed by making the sensor a disposable patch integrated into the garment; activation is solved by leveraging inherent moisture or the user’s perspiration as the rehydration trigger.

Homework question from Ally Huang

1. Background Information: Microgravity and cosmic radiation cause significant muscle atrophy and DNA damage in astronauts. Monitoring real-time protein expression in space is difficult due to bulky equipment. BioBits® allows for rapid diagnostic tests on the ISS with a minimal footprint.
2. Target: Myostatin (MSTN) protein levels, which are key regulators of muscle growth and indicators of muscle wasting.
3. Relation to Space Biology: Tracking Myostatin levels allows researchers to quantify the rate of muscle degradation in microgravity. By using a cell-free biosensor, we can monitor these levels in real-time without needing to return samples to Earth.
4. Hypothesis/Research Goal: Hypothesis: BioBits® can be engineered to produce a fluorescent signal proportional to the concentration of Myostatin mRNA. The goal is to create a “just-add-sample” diagnostic kit for astronauts to monitor their physical health during long-duration missions.
5. Experimental Plan: We will test astronaut saliva samples. Control: Rehydrated BioBits® with a known concentration of MSTN DNA. Experimental: BioBits® rehydrated with the astronaut’s sample. Data will be collected using the P51 Molecular Fluorescence Viewer to observe the intensity of the green fluorescence.

Part B: Final Project Integration — DermLogic

Aim 1: Cell-Free Logic Validation

The core of my final project, DermLogic, relies on the E. coli BL21 DE3 cell-free system to execute a complex molecular AND-gate. This week’s focus on cell-free systems directly informs my strategy for:

• Signal Processing: Using dual-channel toehold switches (L1-GFP and E6/E7-mCherry) to stratify HPV risk.
• On-Demand Therapeutics: Leveraging the “open” nature of cell-free systems to synthesize ~21 nt antisense RNA molecules immediately upon pathogen detection.

Lyophilization Strategy

Following the principles of BioBits®, DermLogic is designed to be a shelf-stable, “just-add-water” (or sample) diagnostic. My validation plan for Aim 1 includes:

1. Cryoprotectant Optimization: Testing a mix of 100mM Trehalose and 0.1% BSA to ensure the T7 RNA Polymerase and ribosomes remain functional after freeze-drying.
2. Stability Testing: Comparing the fluorescence kinetics of fresh vs. rehydrated pellets using the Spark Plate Reader to calculate signal retention.

Key Component: By encapsulating this reaction in a POPC/Cholesterol lipid bilayer with alpha-hemolysin pores, the system transforms from a bulk reaction into a “synthetic cell” capable of localized therapeutic delivery.

Week 10 HW: Advanced Imaging & Measurement Technology

Homework: Final Project

1. Identify at least one aspect of your project that you will measure.

For the DermLogic v2 platform, the primary measurement is the fluorescence intensity generated by the reconstitution of split-sfGFP. This serves as a quantitative proxy for the presence of the target HPV16 L1 and E6 mRNA biomarkers. Additionally, I will measure the molecular weight and structural integrity of the transcribed RNA triggers and the synthesized DNA inserts to ensure the “AND-gate” logic components are correct before assembly.

2. Describe the elements you would like to measure and how you will perform these measurements.

• Transcriptional Accuracy: I will measure the length and purity of the RNA triggers (L1 and E6) produced via T7 in vitro transcription.
• Logic Gate Kinetics (AND-gate): I will measure the rate of sfGFP complementation (fluorescence over time). This requires monitoring four conditions: a negative control, each trigger individually, and the dual-trigger state to confirm that signal only occurs in the presence of both.
• Therapeutic Module Validation: I will measure the expression of the 116 nt antisense E6 RNA segment. Since this is co-transcribed with the reporter, I must verify it is not degraded and maintains its predicted size.
• Protein Fragment Identity: I need to confirm that the split-sfGFP fragments (N-terminal 1-157 and C-terminal 158-238) are being synthesized correctly within the PURExpress cell-free system.

3. What technologies will you use? Describe in detail.

To validate the Experimental Design of DermLogic v2, I will use:

• Fluorescence Spectroscopy (Plate Reader): I will use a microplate reader set to Ex 485nm / Em 507nm to perform real-time kinetic assays at 37°C. This allows for the quantification of the sfGFP reconstitution speed and the establishment of the Limit of Detection (LoD).
• Urea-PAGE (Polyacrylamide Gel Electrophoresis): As specified in Aim 3 of my proposal, I will use Urea-PAGE under denaturing conditions to resolve the RNA transcripts. This is critical for confirming the presence of the antisense/scaffold band and ensuring no premature transcriptional termination.
• Liquid Chromatography-Mass Spectrometry (LC-MS): Inspired by the Week 10 Lab, I would use Intact Mass Analysis on a system like the Waters Xevo G3 QTof to confirm the molecular weight of the synthesized split-sfGFP fragments.
• Peptide Mapping (MS/MS): I would use tryptic digestion followed by tandem mass spectrometry to confirm the amino acid sequence of the split-junction (residues 157/158), ensuring the fragments are prepared exactly as designed for optimal reconstitution.

Week 11 — Bioproduction & Cloud Labs

Part A: The 1,536 Pixel Artwork Canvas

• My Contribution: I was trying to make a buddha/baby at the bottom center.
• What I liked about the project: It was amazing to see it develop as soon as the email went out and during the class. It was a very surreal experience and the spirit of collaboration is really visible when you roll back the history and how it became its final version. So grateful to be a part of this amazing cohort of humans in the loop. Suggestions for improvement: Lets keep this spirit going and make this dream happen; of cloud labs around the world that conduct synthetic biology in the spirit of collaboration and ethics to advance synthetic biology in the least harmful most useful direction. We did it during covid, lets not wait for another pandemic to come together. Thanks Ronan and the team for giving us the taste of this global spirit.

Part B: Cell-Free Protein Synthesis

1. Component Descriptions

2. Master Mix Comparison

• Difference: Usually, the “High-Yield” vs “Standard” versions come down to the specific energy regeneration system (like using 3-PGA instead of Creatine Phosphate) and a finely tuned Magnesium concentration optimized for high-speed output. Basically the use of MonoPhasphates allows for the reuptake of the Phosphates that are discarded after being used as the backbone for the mRNA synthesis.

3. Bonus: Transcription without GMP

• Mechanism: In Transcription RNA Polymerase primarily utilizes the triphosphate form (GTP) to build the RNA chain. But if you just add guanine it is used as a raw material precursor to GMP and GTP which occurs with a series of enzymatic steps using the endogenous cellular machinery in the lysate. And this upccyling of phasphates provides the NMP Robose Mic the extra time of cell free reaction.

Part C: Planning the Global Experiment

1. Fluorescent Protein Analysis

• sfGFP: Superfolder GFP. The reliable workhorse of the lab; it folds fast and stays bright even when the conditions get a bit messy.
• mRFP1: The monomeric red standard. Great for multi-color tagging without the proteins clumping together.
• mKO2: Monomeric Kusabira Orange. A super bright orange that gives a great third channel for logic gate visualization.
• mTurquoise2: A cyan protein that’s incredibly stable—the “gold standard” when you need a high-end FRET donor.
• mScarlet_I: One of the brightest synthetic reds available today; it really pops against the background.
• Electra2: A specialized, highly engineered fluorescent tool designed for specific high-performance assays in cloud lab environments.

2. Experimental Hypothesis

Target Protein: mTurquoise2 with mKO2 for salt-stress analysis

Adjusted Reagent(s): Magnesium Glutamate, Glucose, and Amino Acid saturation.

Expected Effect: I hypothesize that maximizing the Magnesium concentration to 15–18 mM will stabilize the ribosome-mRNA complexes against the high ionic strength of the locked 312.5 mM Potassium Glutamate baseline. Furthermore, I expect that supplementing the reaction with 3.0 g/L Glucose and a surplus of Amino Acids (5–6 mM) will prevent metabolic exhaustion over the 36-hour incubation period, resulting in a sustained fluorescence signal and higher total protein yield compared to the standard NMP-Ribose-Glucose master mix.

3: Final Reagent Supplement JSON

For the global experiment, I designed a 6-well optimization cluster to test the limits of molecular crowding and metabolic longevity in a high-salt environment (locked at ~312mM Potassium Glutamate).

[
  {
    "quadrant": "Q4",
    "well_label": "J2",
    "supplements": [
      { "id": "nuclease_free_water", "supplemental_volume_nl": 1050 },
      { "id": "magnesium_glutamate", "supplemental_volume_nl": 325 },
      { "id": "aa_mix_17", "supplemental_volume_nl": 375 },
      { "id": "glucose", "supplemental_volume_nl": 75 },
      { "id": "nicotinamide", "supplemental_volume_nl": 175 }
    ]
  },
  {
    "quadrant": "Q4",
    "well_label": "J3",
    "supplements": [
      { "id": "nuclease_free_water", "supplemental_volume_nl": 1550 },
      { "id": "magnesium_glutamate", "supplemental_volume_nl": 450 }
    ]
  },
  {
    "quadrant": "Q4",
    "well_label": "J4",
    "supplements": [
      { "id": "nuclease_free_water", "supplemental_volume_nl": 850 },
      { "id": "aa_mix_17", "supplemental_volume_nl": 775 },
      { "id": "tyrosine", "supplemental_volume_nl": 375 }
    ]
  },
  {
    "quadrant": "Q4",
    "well_label": "K2",
    "supplements": [
      { "id": "nuclease_free_water", "supplemental_volume_nl": 1825 },
      { "id": "glucose", "supplemental_volume_nl": 175 }
    ]
  },
  {
    "quadrant": "Q4",
    "well_label": "K3",
    "supplements": [
      { "id": "nuclease_free_water", "supplemental_volume_nl": 1425 },
      { "id": "nicotinamide", "supplemental_volume_nl": 575 }
    ]
  },
  {
    "quadrant": "Q4",
    "well_label": "K4",
    "supplements": [
      { "id": "nuclease_free_water", "supplemental_volume_nl": 1125 },
      { "id": "magnesium_glutamate", "supplemental_volume_nl": 325 },
      { "id": "aa_mix_17", "supplemental_volume_nl": 375 },
      { "id": "glucose", "supplemental_volume_nl": 175 }
    ]
  }
]

Part D: Build-A-Cloud-Lab (Optional)

• Since I’m working on the DermLogic skin patch for HPV detection, I’m interested in how the Nebula RAC could prototype dual-channel fluorescence sensors. Seeing this level of remote automation makes the “sandbox lab” dream feel much more attainable for an entrepreneur.