Week 1 HW: Principles and Practices

To make things tidy, I decided to answer most of the questions about the biological engineering application in sections of a separate project idea page.

PLEASE NOTE: on the project page accessible through the link I have been unable to show two images: an image of Van Gogh’s Sunflowers painting and a diagram showing how I’ve scored the actions I’ve suggested. It doesn’t appear to load the image, yet the build log indicates it’s a success. I’m not sure what the problem is but it means you won’t see those two pictures.

Q1. First, describe a biological engineering application or tool you want to develop and why.

See Section 1: Project background

Q2 Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals.

See Section 2: Developing a policy framework to make the project contribute to an ethical future of bioengineering

Q3 Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of “Failure” and “Success”).

See Section 3 Ideation of actions to support policy framework

Q4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals.

See 4 Evaluating effectiveness of actions that support policy goals

Q5. Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties

See 5 Discussions of priorities and assumptions

Lecture Preparation for Week 2

Questions relating to Professor Jacobson’s slides

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?
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 1 The slides and other sources indicate DNA polymerases have a error frequencies of about 10-6 mutations/bp and the human genome has between 3,100,000,000 and 3,200,000,000 base pairs of DNA. ^{1 2} Cells employ various repair mechanisms. Some errors are corrected during replication through a process called proofreading. After replication, mismatch repairs can reduce the rate even further. ³

Answer 2 There are very many ways that proteins could be coded, but most of the combinations do not result in stable three dimensional conformations. The 4th edition of the Molecular Biology of the Cell notes that: “Since each of the 20 amino acids is chemically distinct and each can, in principle, occur at any position in a protein chain, there are 20 × 20 × 20 × 20 = 160,000 different possible polypeptide chains four amino acids long, or 20n different possible polypeptide chains n amino acids long. For a typical protein length of about 300 amino acids, more than 10390 (20300) different polypeptide chains could theoretically be made. This is such an enormous number that to produce just one molecule of each kind would require many more atoms than exist in the universe.

Only a very small fraction of this vast set of conceivable polypeptide chains would adopt a single, stable three-dimensional conformation—by some estimates, less than one in a billion. The vast majority of possible protein molecules could adopt many conformations of roughly equal stability, each conformation having different chemical properties. And yet virtually all proteins present in cells adopt unique and stable conformations.” ⁴

Questions relating to Dr. Le Proust’s slides:

1. What’s the most commonly used method for oligo synthesis currently?
2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
3. Why can’t you make a 2000bp gene via direct oligo synthesis?

Answer 1: Solid-phase phosphoramidite chemistry. According to Twist Bioscience: “Phosphoramidite chemistry is the gold standard method for DNA synthesis that has been used in the industry for almost 35 years. Since its discovery, its simplicity and high efficiency have allowed large volumes of oligonucleotide sequences to be synthesized up to 200 base pairs in length. Currently, it is the only commercially viable chemistry able to provide the volume of DNA required by the synthetic biology market.” ⁵

Answer 2 According to Pichon, it is difficult to synthesise sequences longer than 150 nucleotides because the theoretical yields for larger sequences is low. When solid phase phosphoramidite chemistry is used to make the sequences, the efficiency of coupling between successive oligonucleotides becomes less efficient as the sequence becomes longer. In using this process, the longer the sequence, the more likely that the resulting sequence will contain insertion or deletion errors. ⁶

Answer 3 If two nucleotides are used to make a single base pair, then a 2000 bp gene would require 4000 nucleotides. If synthesising sequences of more than 200 nucleotides is difficult, much more than that would be impractical. Yin describes how “…the state of the art chemical synthesis methods cannot reliably produce oligos longer than 200 nt.” The author explains that for some longer sequences, “…if a sequence contains higher order structures with unusual stability, the PCR assembly method may not function effectively.” ⁷

Question about Prof. Church’s slide #4

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

Answer According to the Cleveland Clinic page on amino acids, the human body needs 20 amino acids to make the all the proteins that make it function properly. However the page and a few other sources only count nine rather than ten essential amino acids. These amino acids are needed by all animals:

• Histidine
• Isoleucine
• Leucine
• Lysine
• Methionine
• Phenylalanine
• Threonine
• Tryptophan
• Valine ⁸

One article names Arginine as a conditionally essential amino acid that is essential in certain life stages or when certain physiological stresses are present. ⁹

If the genetically altered dinosaurs could not produce lysine, they would presumably die before they even reached maturity.

Week 2 DNA Read, Write and Edit

Assignment 2

I created a Benchling account, loaded up the Lambda DNA, and then tried different combinations of the following restriction enzymes.

• EcoRI
• HindIII
• BamHI
• KpnI
• EcoRV
• SacI
• SalI

I note that the Automation Art tools produces randomly created electrophoresis ladders, but I excluded Ndel, Pvull and Xhol - because they were not in the list we were supposed to use.

I found it difficult to produce virtual digest ladders that could be combined into recognisable artistic shapes. To get an idea of the range of possible patterns, I systematically looked at choosing combinations of 1, 2, 3, 4 and 5 enzymes.

I’ll include some examples of this brute force way of assessing potential patterns. But that didn’t work well. So I widened my enzyme list to include the enzyme Pvull as well. I ended up with this pattern:

Part 3: DNA Design Challenge

3.1

I’ve chosen the sequence for staphyloxanthin, the protein found in the bacteria Staphylococcus aureus that creates a deep yellow colour that might be similar to the chrome yellow pigment van Gogh used to paint the centres of his sunflowers in the ‘Sunflowers’ painting hanging in the National Gallery.

3.2 Reverse translate

I found the Uniprot entry for 4,4’-diapophytoene synthase, which is used in the biosynthesis of the yellow-orange carotenoid staphyloxanthin. In Benchling, I imported an AA sequence and specified ‘A9JQL9’ and ‘Uniprot’ to import from a database. I selected the entire protein sequence, right-clicked and clicked ‘Backtranslate’. From there, I obtained this DNA Sequence:

ATGACTATGATGGATATGAATTTCAAATATTGTCATAAAATAATGAAAAAACACAGTAAAAGTTTCTCTTATGCCTTTGATTTACTTCCAGAAGACCAAAGAAAGGCTGTATGGGCAATTTATGCAGTTTGTCGCAAAATTGATGACTCAATAGATGTTTATGGTGACATTCAATTTTTAAATCAAATAAAGGAAGATATTCAATCTATAGAAAAATATCCATACGAATATCATCATTTTCAAAGTGATAGAAGAATTATGATGGCACTACAGCACGTGGCTCAACATAAAAATATTGCTTTCCAGAGCTTTTATAATCTTATTGATACCGTCTATAAAGATCAACATTTTACAATGTTTGAAACTGATGCGGAGTTATTCGGATATTGCTATGGTGTTGCTGGTACAGTTGGTGAAGTCTTAACACCTATCTTATCAGATCATGAAACGCATCAAACATATGACGTGGCGCGTCGTCTTGGAGAATCATTGCAATTAATTAATATTTTAAGAGATGTAGGCGAGGATTTTGAAAATGAACGTATTTACTTTTCAAAACAACGACTAAAACAATATGAGGTAGATATTGCTGAAGTTTATCAAAATGGGGTAAACAACCATTATATTGATTTATGGGAATATTACGCAGCAATCGCAGAAAAAGATTTTCGAGATGTTATGGATCAAATTAAAGTATTTTCTATTGAAGCACAACCTATAATAGAACTCGCCGCACGTATCTATATCGAAATATTAGATGAAGTTAGACAAGCTAATTATACTTTGCACGAAAGAGTATTTGTGGAAAAACGTAAGAAAGCTAAGTTATTTCATGAGATTAATTCGAAATACCATAGGATT

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?

I used Vector Builder’s Codon Optimisation Tool and specified the sequence from 3.2 to be inserted into E coli strain K-12 substr: MG1655.

ATGACTATGATGGATATGAATTTCAAATATTGCCACAAAATTATGAAAAAACACAGCAAAAGCTTTAGCTATGCGTTTGATCTGCTGCCCGAAGATCAGCGCAAAGCCGTGTGGGCCATTTACGCGGTGTGTCGTAAAATTGACGATAGCATTGATGTGTACGGCGATATTCAGTTTCTGAATCAGATTAAAGAAGATATTCAGAGCATTGAAAAATACCCGTACGAATATCATCATTTTCAGTCAGATCGCCGCATTATGATGGCCCTGCAGCATGTGGCGCAACATAAAAACATTGCGTTTCAGAGCTTCTACAACCTGATTGATACCGTGTATAAAGATCAGCATTTTACCATGTTTGAAACCGATGCGGAACTGTTCGGCTACTGTTATGGCGTGGCCGGCACCGTGGGCGAAGTGCTGACCCCGATTCTGAGCGATCATGAAACCCACCAGACCTACGATGTGGCCCGCCGCCTGGGCGAAAGCCTGCAGCTGATTAATATTCTGCGCGATGTGGGCGAAGATTTTGAAAACGAACGTATTTATTTCTCGAAACAGCGTCTGAAACAGTATGAAGTGGATATTGCGGAAGTTTACCAGAATGGCGTGAACAATCATTATATTGATCTGTGGGAATATTATGCGGCGATCGCGGAAAAAGACTTTCGCGATGTGATGGATCAGATTAAAGTGTTCTCAATTGAAGCGCAGCCGATTATTGAACTGGCCGCGCGCATTTATATTGAAATTCTGGATGAAGTGCGCCAGGCCAATTATACCTTACATGAACGCGTGTTTGTGGAAAAACGTAAAAAAGCCAAACTGTTTCACGAAATTAACAGCAAATACCATCGCATT

3.4. You have a sequence! Now what?

I’d follow the instructions in Homework Week 2 ‘Prepare a Twist DNA Synthesis Order’ to create an expression cassette, which would be designed to drop into a plasmid vector. This would involve specifying a sequence comprising: a promoter, a Ribosome Binding Site (RBS), a start codon, the optimised codon sequence that will be designed to make the staphyloxanthin work best with E. coli, the 7x His Tag, a stop codon and a terminator. I’d carefully annotate each of these components and export a Fasta file that I would then upload to Twist. The order from twist would presumably arrive in London as the freeze-dried sequence for the cassette.

Next, I’d use PCR to amplify the insert sequence (comprising all the components I specified in Benchling) and then add it to a tube that contains the three enzymes used in Gibson Assembly cloning. First, the T5 exonuclease would cause the 5’ ends of the insert sequence and linearised vector (plasmid backbone) to be chewed back to create overlaps. Polymerase would begin to fill in at the overhangs to prevent excessive enzyme chewing. When the overhangs become stable, taq ligase would complete the fusion of the inserted staphyloxanthin sequence into the backbone sequence of the plasmid. The recommended temperature for this would be about 50’C.

In this process, the enzymes would cut at specific places on the plasmid, which would create a gap for the inserted sequence. I’d need to select appropriate primers: a forward primer that would create an overlap between the sequence leading to the beginning of the gap and the beginning of the inserted sequence. Then I’d need to pick a reverse primer that would create an overlap between the end of the inserted sequence and the beginning of the sequence that starts at the other end of the gap. The end product would be a plasmid where the sequence had been inserted into the gap.

The plasmids would be then be inserted into E. coli by applying temporary heat shock to the cells. The shock would create temporary pores in the membrane of the bacteria which would allow the plasmid to pass inside. Once inside, the E. coli’s own machinery would express the genes encoded into the plasmids as if it were its own. The promoter encoded into the insert sequence would signal the optimised staphyloxanthin sequence to be first transcribed into RNA and then later translated into a protein that would then produce the yellow colour associated with the staphyloxanthin gene.

Part 4: Prepare a Twist DNA Synthesis Order

I went through all the instructions for this part using Benchling and Twist, and the final plasmid looked like this in Benchling:

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 would want to sequence the protein Proteorhodopsin. When I was working on my Masters dissertation in Art History, I came across this paper: “Frangipane, Giacomo, et al. “Dynamic density shaping of photokinetic E. coli.” Elife 7 (2018): e36608.”

The authors had created E. coli that used proteorhodopsin to make them respond to light. When light was shone on them, it provided energy that propelled the bacteria. They were able to manipulate combinations of light and darkness to help them clump together in a way that looked like a photographic negative.

(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? Also answer the following questions: I would use Illumina Sequencing (Short-Read), because they have often been used for metagenomic and transcriptomic sequencing of the genes for Photorhodopsin.

This paper about microbial rhodopsin dynamics by Laura Gómez-Consarnau makes use of the Illumina PE150 platform: Gómez-Consarnau, Laura, et al. “Unexpected microbial rhodopsin dynamics in sync with phytoplankton blooms.” Nature Communications (2025).

Is your method first-, second- or third-generation or other? How so? The method uses short reads and is an example of Next Generation (Second generation) sequencing.

What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.

I read this paper by Dahui Qin to learn more about how NGS worked. Its main steps include:

1. DNA Fragmentation. Involves breaking up DNA into many short segements using various methods such as enzymatic digestion. The relevant sequences of interest are isolated using complementary probes.

2. Library preparation. This involves preparing the segments in a uniform way that makes it easier for sequencing primers to bind to fragments.

3. Sequencing. Allows massive parallel sequencing of all the fragments at the same time.

4. Analysis. Bioinformatics analysis tools are used to support base calling, read alignment, variant identification and variant annotation. The fragments are compared with a reference sequence to identify mutations and then all the fragment sequences are stitched together to create a complete sequence.

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! :)

As part of my final project, I’m looking at doing something similar to Part 4, but using a plasmid where sfGFP can be expressed in the presence of high levels of carbon dioxide. From what I’ve been gathering, the easiest way of doing this is to make a less specific function that would make it fluoresce green in the presence of (carbonic) acid. The main drawback is that it would then react to any acid, not just acid resulting from carbon dioxide dissolved in water. Another approach involves having the bacteria detect HC03 but I’m not yet sure how to do that.

I would be looking at a whole-cell biosensing bacteria that would likely use a cadBA promoter, which is supposed to be active as an acid stress response in E. coli. The promoter would act as a kind of switch to turn the GFP on.

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:

I would be using Twist to synethesise the plasmid-containing bacteria that would fluoresce green in the presence of CO2, probably indirectly triggered by a response to an acid. So perhaps I’d be building a biosensor that was more about detecting acid than CO2.

What are the essential steps of your chosen sequencing methods? What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?

I’m not sure if this is relevant to what I’d like to do. I’m not wanting to synthesize DNA based on something that hasn’t been sequenced before. I’m actually more interested in using a documented promoter to turn the GFP on. I’d be doing something really similar to Part 4.

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?

I’d want to edit the E. coli sequence so I can stuff the circuit I want into it.

(ii) What technology or technologies would you use to perform these DNA edits and why? Also answer the following questions: I’d be using Twist to synthesise a genetically altered E. coli that would glow green in the presence of CO2 or perhaps a strong acid stress instead.

How does your technology of choice edit DNA? What are the essential steps? 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? I would be editing a plasmid in the way Part 4 outlines and getting Twist to do it.

What are the limitations of your editing methods (if any) in terms of efficiency or precision?

Week 3 Lab Automation

Assignment 3

Following on from last week’s assignment, I decided to use Vincent van Gogh’s Sunflowers painting (the one hanging in the National Gallery) for my art subject. I tried to download an image of the painting and upload it to the Opentrons automated art interface. Importing it made some artistic effects I didn’t want - it flooded the background with yellow, left out the blue streaks and didn’t do much to distinguish between orange and yellows. Importing it created something that wasn’t recognisable.

I was really impressed by the tool but opted to create my own from scratch. I made some decisions like hollowing out some of the flower petals because if I filled them all with orange and yellow they would look unrecognisable. I could have used the automation art interface to hand craft the image, but I wanted to learn more about the mechanics of how the opentrons commands would work rather than immediately rely on automatically generated code.

Like the autogenerated code, I concluded it would be more efficient to do successive passes of placing droplets by colour: first one colour, then another and the next. I spaced my droplets at 2.5 mm. I tried using a larger distance between drops, but the resolution dropped and I couldn’t make a recognisable painting of Sunflowers with the space I had available. If they bleed together, that might actually work - because the painting is itself slightly abstract and it wasn’t meant to appear to be too realistic.

Note that because the lab only had colours for blue, pink and purple in stock, I had to rework my art. I call this one ‘Rattlesnake Dinner’ - it is meant to show how a snake might view a mouse through a thermal vision perspective.

Post Lab Questions

Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications. The paper I chose was: Taguchi S, Matsuzawa R, Suda Y, Irie K, Ozaki H. Investigating the effects of liquid handling robot pipetting speed on yeast growth and gene expression using growth assays and RNA-seq. Micropublication Biology. 2025 May 13;2025:10-7912. Available here

The paper notes that: “.the influence of pipetting speed on biological experiments, —particularly when systematically varied using liquid-handling robots and evaluated through gene expression and cell growth—remains poorly investigated.” It conducted multiple experimental runs on an Opentrons to determine how the variation in pipetting speed influenced gene expression of Saccharomyces cerevisiae.

The authors write: “In conclusion, within the range of pipetting speeds investigated, variations in pipetting speed did not impact the maximum relative growth rate and the gene expression profiles of yeast.” The finding implies that if the Opentrons OT-2 were run at its top pipetting speed, there would not be much difference in gene expression of yeast colonies.

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.

For my final project, I plan to 3D print multiple objects based on models derived from various Green Man sculptures found in architecture (and particularly churches). I have already begun to use the application Polycam to take hundreds of pictures of Green Man figures in Ely Cathedral. I’ve been able to convert those into *.STL files which I hope to eventually print in the Lifefabs Lab. I will be experimenting with which sculpture provides the best surface properties to grow colonies of GFP-containing bacteria.

Week 4 Protein Design Part 1

Part A: Conceptual Questions

Choosing 9 of 11 questions to answer

Q1. 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) I’ve made some assumptions:

• The meat mass does not include air or water.
• The meat mass does not include organic materials that are not amino acids

A Dalton is a unit of mass used to express atomic and molecular weights. I used this converter to determine how many Daltons are in 500 grams of organic material. 500 g = 3.011086821E+26 daltons. If an amino acid is 100 daltons, then 500 g would contain 3.011086821E+24 molecules.

Q2 Why do humans eat beef but do not become a cow, eat fish but do not become fish? When humans digest beef, the process breaks the organic materials down into base materials that their bodies can use to build amino acids according to human DNA. The DNA found in the beef is not transferred and does not displace the DNA in our bodies.

Q3 Why are there only 20 natural amino acids? Doig examines why the 20 standard amino acids were selected by Nature. ¹ One theory is that by chance those 20 were selected and became established when there could have been alternative sets. But Doig seems to think their properties alone and in combination provided a kind of almost ideal set of amino acids. He writes:

“…they were selected to enable the formation of soluble structures with close-packed cores, allowing the presence of ordered binding pockets. Factors to take into account when assessing why a particular amino acid might be used include its component atoms, functional groups, biosynthetic cost, use in a protein core or on the surface, solubility and stability. Applying these criteria to the 20 standard amino acids, and considering some other simple alternatives that are not used, we find that there are excellent reasons for the selection of every amino acid. Rather than being a frozen accident, the set of amino acids selected appears to be near ideal.”

Q4 Can you make other non-natural amino acids? Design some new amino acids.

Q5 Where did amino acids come from before enzymes that make them, and before life started? Cowing explains that amino acids could have formed in the early planetesimal bodies of the early solar system, far from the sun ². Those bodies would contain large amounts of ice that could have been melted by the heat produced by radioactive materials. The melted water could have interacted with other volatile compounds to form amino acids. These could have been contained in meteorites which struck Earth, providing a large concentration of amino acids which could have led to life on Earth.

Amino acids could have also been created through chemical synthesis reactions happening on Earth. In 1953, Miller and Urey combined ammonia, hydrogen, methane and water vapour and in a flask and subjected it to electrical sparks. From this experiment, they were able to create eleven standard amino acids.³

Q6 If you make an α-helix using D-amino acids, what handedness (right or left) would you expect? I would expect it would be left-handed. Novotny writes: “a-Helices composed of L-amino acids are energetically more favourable in a right-handed conformation than in the left-handed mirror image of this arrangement due to steric hindrance between side-chain atoms and the main-chain carbonyl moiety. Conversely, D-amino acids will form more stable a-helices with a left-handed than with a righthanded conformation” ⁴

Q7 Can you discover additional helices in proteins? So far in class we’ve discussed the alpha helix. In Tamar’s “Protein Structure Hierarchy”, the author describes the pi helix and the collagen helix. ⁵

Q8 Why are most molecular helices right-handed? Because d-sugars direct the formation of right-handed helices and left hand versions don’t fit well.

MacDermott explains: “Biopolymer chirality is definitely determined by monomer chirality: l-amino acids can form only right-handed α-helices in protein secondary structures, and DNA naturally coils up into a right rather than left-hand B-form double helix because it is made of d-sugars. Why? Because of diastereomeric effects: d-sugars automatically direct the formation of a right-hand B-form double helix, because they simply do not fit well into a left-hand version. It is true that DNA does sometimes form a left-hand double helix, but this ‘Z-form’ helix is not a mirror image of the B-form, it has a totally different backbone conformation in order to be able to accommodate the d-sugars in a left-hand helix” ⁶

Eric Lindahl’s Youtube video explains that a helix such as the alpha helix is right-handed because it is due to L amino acids. ⁷

As a paper by Cole notes: “The remarkable predominance of right-handedness in beta-alpha-beta helical crossovers has been previously explained in terms of thermodynamic stability and kinetic accessibility.” ⁸

Q9a Why do beta sheets tend to aggregate? Beta sheets are prone to intermolecular hydrogen bonding at the sheet edges with other beta sheets. Other parts of beta sheets are hydrophobic. They will tend to be attracted to each other and repelled by water, which will tend to bring them together.

Q9b What is the driving force for β-sheet aggregation? Hydrophobic forces tend to repel beta sheets from water towards each other. Once they are close together, the attractive forces from intemolecular hydrogen bonding orders them together.

Q10. Why do many amyloid diseases form β-sheets? Can you use amyloid β-sheets as materials?

Q11. Design a β-sheet motif that forms a well-ordered structure.

Part B: Protein Analysis and Visualisation

1. Briefly describe the protein you selected and why you selected it. I’ve chosen rhodopsin, whic is a light-sensitive receptor protein found in the rod cells of the retina, and important for dim light vision. ⁹. The human protein for rhodopsin is described in P08100 · OPSD_HUMAN.

2. Identify the amino acid sequence of your protein. The sequence is: MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA

2a. How long is it? The sequence is 348 amino acids long.

2b. What is the most frequent amino acid? You can use this Colab notebook to count the frequency of amino acids. The most frequent amino acid is ‘Alanine’, which appears 32 times.

2c. How many protein sequence homologs are there for your protein? Hint: Use Uniprot’s BLAST tool to search for homologs. By using (Uniprot BLAST)[https://www.uniprot.org/blast] I identified 5 homologs belonging to the Chimpanzee, Northern white-cheeked gibbon, and the Western lowland gorilla.

2e. Does your protein belong to any protein family? Rhodopsin belongs to the family of G protein–coupled receptors (GPCRs). ¹⁰

3. Identify the structure page of your protein in RCSB I entered the Uniprot ID for rhodopsin, P08100, into the RCSB search and ran the query. I then visualised the 3D representation through the page and obtained this result:

3a. 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 appears to have first been released between 2015 and 2016. Its resolution is 3.3 Å

3b. Are there any other molecules in the solved structure apart from protein? The page is subtitled: “Crystal structure of rhodopsin bound to arrestin by femtosecond X-ray laser”. Arrestin is another protein.

3c. Does your protein belong to any structure classification family? It belongs to a GPCR superfamily. A paper by Zhou indicates: “Rhodopsin is a member of class A of the GPCR superfamily, which is a large group of cell surface signaling receptors that transduce extracellular signals into intracellular pathways through the activation of heterotrimeric G proteins” ¹¹ I tried typing entering ‘p08100’ into the EBI’s SCOP tool and used this query, yielded two domains. I clicked on 5W0P A:1-324 which indicated it was a member of the Class A (rhodopsin) G protein-coupled receptor-like’

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”.

First I downloaded and installed PyMol. Then I searched for the PDB ID for ‘rhodopsin’ and obtained the ID 6QNO. I loaded the PDB structure for 6QNO and it provided me this:

Cartoon Ribbon Ball and stick

Color the protein by secondary structure. Does it have more helices or sheets? I think it has more sheets. In this diagram, helices are coloured red and cover 3067 atoms. Beta sheets are coloured in yellow and cover 2845 atoms. If we count the distinct number of shapes rather than focus on atom coverage, I think there are more helices.

Color the protein by residue type. What can you tell about the distribution of hydrophobic vs hydrophilic residues?

I coloured hydrophilic residues blue and hydrophobic residues yellow. I observe that one bunch of hydrophobic residues tend to concentrate together in yellow on the left.

Visualize the surface of the protein. Does it have any “holes” (aka binding pockets)? I visualised the surface, rotated it and found two holes that I’ve highlighted in thick orange squares.

Part C: Using ML-Based Protein Design Tools

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

I’ve used the sequence for rhodopsin: MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA

C1. Protein Language Modeling

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

Can you explain any particular pattern? (choose a residue and a mutation that stands out) (Bonus) Find sequences for which we have experimental scans, and compare the prediction of the language model to experiment.

I looked at the scan and noticed that at position ‘8’ there were some yellow bars. Counting from 0 to 8 inclusively, the sequence ends in ‘F’, which has a model score of

model score = 0.5256555 012345678 MNGTEGPNF

model score = 2.980395 012345678 MNGTEGPNS

model score = 3.427129 012345678 MNGTEGPNT

If I assume that the higher the model score, the more likely an amino acid will occupy the position, it suggests that although ‘F’ appears in the rhodopsin sequence, in other proteins that begin with MNGTEGPN are more likely to have a ‘T’. For any of the dark blue squares (which tend to be very negative) I would assume their presence in the amino acid would be unstable or cause harm.

Latent Space Analysis Use the provided sequence dataset to embed proteins in reduced dimensionality.

Analyze the different formed neighborhoods: do they approximate similar proteins? I think they do. I zoomed into the map and tried to pick two very close neighbours. My plan was to find the Uniprot IDs for neighbouring proteins and do a Protein blast sequence to determine how similar they were to one another. But I found it difficult to get IDs for neighbouring pairs.

I zoomed in on these two data points:

Up close, this is what those two dots represented:

I tried to find Uniprot entries for both of these for each

• Putative arsenical resistance operon repressor AF0168 Archaeoglobus fulgidus
• C-terminal fragment of elongation factor SelB Moorella thermoacetica

I downloaded FASTA files for each. I then went to the NCBI’s BLASTP facility:

I loaded the FASTA files and then waited for the blast to complete. The results indicated they don’t appear to have much similarity with one another and I’m not sure why - that doesn’t fit well with the apparent similarity suggested by the close collocation of the proteins in the visualisation.

Place your protein in the resulting map and explain its position and similarity to its neighbors.

C2. Protein Folding

Fold your protein with ESMFold. Do the predicted coordinates match your original structure? Try changing the sequence, first try some mutations, then large segments. Is your protein structure resilient to mutations?** First I tried to fold the rhodopsin protein with no changes, and then in the third position where I substituted a ‘F’ for a ‘T’. There wasn’t much change. But then I made the sequence include a lot of Cs and Qs and I started to notice the left end started to distort a lot.

Rhodopsin protein with no changes.

Rhodopsin protein with many alterations.

Part D. Group Brainstorm on Bacteriophage Engineering

Brainstorm Session Choose one or two main goals from the list that you think you can address computationally (e.g., “We’ll try to stabilize the lysis protein,” or “We’ll attempt to disrupt its interaction with E. coli DnaJ.”). Write a 1-page proposal (bullet points or short paragraphs) describing: Which tools/approaches from recitation you propose using (e.g., “Use Protein Language Models to do in silico mutagenesis, then AlphaFold-Multimer to check complexes.”). Why do you think those tools might help solve your chosen sub-problem? Name one or two potential pitfalls (e.g., “We lack enough training data on phage–bacteria interactions.”). Include a schematic of your pipeline. This resource may be useful: HTGAA Protein Engineering Tools Each individually put your plan on your HTGAA website Include your group’s short plan for engineering a bacteriophage

For my review of the Bacteriophage Final Projects Goals for engineering the L Protein, I focused on the goal of designing bacteriophages that showed increased stability. Initially I prompted ChatGPT to help determine how researchers might stabilise a lysis protein that is created by bacteriophages used to kill e coli¹² According to the response, the challenges for making stable bacteriophages include the risk of denaturation at higher temperatures or broad ranges of pH, degradation by bacterial enzymes, and the risk of aggregation or precipitation when the bacteriophage is purified for therapeutic use. It recommended four strategies to consider:

• rational design, which is: “…a molecular method aimed at altering the genetic makeup of existing enzymes to improve their structural and functional properties in a predictable manner, relying on prior knowledge of the enzyme’s molecular details”¹³
• fusion proteins, which are: “…A fusion protein is a single polypeptide composed of two or more distinct protein domains encoded initially by separate genes but artificially linked to function as one unit.” They are often incorporate tags into a protein which help improve the solubility, purification and detection of the main protein of interest.¹⁴
• directed evolution, which involves “… creating mutants by random mutagenesis and recombination followed by the screening of mutants for desired characteristics such as increase stability and changed catalytic specificity”¹⁵
• expression optimisation, which: “…focuses on maximizing the yield of proteins or other biological molecules in various expression systems” ¹⁶

I’d focus on directed evolution, and perhaps randomly introducing random mutations in an amino acid sequence for the L protein, and using ESM2 to help identify which ones would be likely to form. I could then try to use tools that tested how well the resulting L proteins would bind to mutated proteins that act as the chaperone sites in the host such as DnaJ. I think the ESM2 model would help me make some intelligent guesses about mutations that would result in a viable protein.

Week 5 Protein Design Part 2

Part 1: Generate Binders with PepMLM

Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation. I visited the Uniprot page for (P00441)[https://www.uniprot.org/uniprotkb/P00441/entry#sequences] and the normal sequence is:

012345… MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

A4V represents one mutation where the ‘A’ changes to a ‘V’ at position 4: MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

I found additional information about the AV4 mutation on the ALS Association’s page

Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card: Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence. To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison. Record the perplexity scores that indicate PepMLM’s confidence in the binders.

I opened my copy of the PepMLM script and entered the sequence. I tried adjusting the slider to change the peptide length from 15 to 12, but the slider appears fixed. So I opened the code and just changed the peptide length to ‘12’. I set the number of peptides to 4. ![Adjusting the PepMLM script]](sod1_a4v_pepMLM1.png)

I then ran the code for ‘Load Model’. Next, I modified ‘Generate Peptides’ so that it would include the known binder FLYRWLPSRRGG I obtained one binder result:

Binder	        Pseudo Perplexity
FLYRWLPSRRGG	20.63523127
WHYYAAALEHGX	11.53622912
WLYYVTAVRWGX	22.31654743
HRYPAAAVAHKX	8.720199577
WRYPVAAAEWGE	14.43921585'

Part 2: Evaluate Binders with AlphaFold3

Navigate to the AlphaFold Server: alphafoldserver.com. For each peptide, submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein-peptide complex. Record the ipTM score and briefly describe where the peptide appears to bind.

For each peptide sequence, I used the alphafold query form as shown:

For the first candidate binder FLYRWLPSRRGG, I obtained this image:

For the binder WRYPVAAAEWGE, I obtained this image:

For the other three binder candidates, I got an error:

These are the results I could gather:

Binder	        ipTM        pTM
FLYRWLPSRRGG	0.22        0.82
WHYYAAALEHGX	Error
WLYYVTAVRWGX	Error
HRYPAAAVAHKX	Error
WRYPVAAAEWGE	0.27        0.82'

Does it localize near the N-terminus where A4V sits? Neither of the two viable binding candidates FLYRWLPSRRGG or WRYPVAAAEWGE appears to bind anywhere near the N terminus (shown in the left part of the diagrams)

Does it engage the β-barrel region or approach the dimer interface? The peptides appear to engage the beginning of the beta sheet.

Does it appear surface-bound or partially buried? Both of the peptides appear to hover over the other protein and seem to me like they may be more surface bound than becoming buried or entangled.

In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated peptide matches or exceeds the known binder. The two binder candidates that are able to work have a similar pTM score. Both are above 0.5 which suggests the overall predicted fold for the complex might be similar to the true structure. Both also have a very low ipTM, which suggests they are low-confidence predictions of relative positions of subunits within the complex. The candidate WRYPVAAAEWGE seems slightly better a candidate than FLYRWLPSRRGG, but not by much. Both seem like poor candidates as they don’t appear to bind anywhere near the N terminus where the mutation exists.

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: Paste the peptide sequence. Paste the A4V mutant SOD1 sequence in the target field. Check the boxes Predicted binding affinity Solubility Hemolysis probability Net charge (pH 7) Molecular weight

For the main suggested candidate FLYRWLPSRRGG, I obtained these results:

For the other binder candidate WRYPVAAAEWGE, I obtained these results:

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?

I observe that WRYPVAAAEWGE, which has a slightly higher ipTM value than FLYRWLPSRRGG, also shows very slightly higher hemolysis and binding affinity properties, but still not much. Both are considered soluble, non-hemolytic and weak binding.

Choose one peptide you would advance and justify your decision briefly. I’m not sure I’d advance either, because their ipTM scores were so low. Yet, it appears that WRYPVAAAEWGE has slightly better properties than FLYRWLPSRRGG.

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.

Open the moPPit Colab linked from the HuggingFace moPPIt model card Make a copy and switch to a GPU runtime. In the notebook: Paste your A4V mutant SOD1 sequence. 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). Set peptide length to 12 amino acids. Enable motif and affinity guidance (and solubility/hemolysis guidance if available). Generate peptides. 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?

I ran the script and tried to specify a motif value of ‘2’ or ‘3’ - but the next script which generated the peptides complained of an error.

I then unselected ‘motif’ and tried to run it. I got the following sets of results:

'YKQYKHKQLCPI' 0.9623022861778736, 0.7793857455253601, 6.442235946655273
'KKEKNKKKCGLS' 0.9733861181885004, 0.9757760167121887, 7.327633857727051
'KDKKKDKYYCTI' 0.9739618953317404, 0.9253293871879578, 7.365080833435059

I would try to insert A4V mutant SOD1 into animal and then human cell lines and see whether using the peptides reduced the problems associated with A4V.

Week 6 Genetic Circuits Part I: Assembly Technologies

Genetic Circuits Part I: Assembly Technologies

1 What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? A product page for Fisher Scientific indicate: “Phusion High-Fidelity PCR Master Mix is convenient 2X mix containing Phusion DNA Polymerase, nucleotides, and optimized reaction buffer including MgCl2. Two master mix formulations are available: with HF Buffer (F-531S and F-531L) and with GC Buffer (F-532S and F-532L).”¹

The optimised reaction buffer is a mix of chemicals that support the best environment to allow DNA polymerase to work during PCR amplification.² The DNA polymerase are enzymes that are used to duplicate the genetic information stored in DNA, generating a faithful copy.³

• Phusion DNA Polymerase
• nucleotides
• optimised reaction buffer

2 What are some factors that determine primer annealing temperature during PCR? The annealing temperature, which is the optimal temperature at which the primers can bind to the DNA, is heavily related to the primer melting temperature and the guanine citosine (GC) content of the primer. The melting temperature of the primer is the temperature at which 50% of the DNA duplex separates and becomes single stranded. The melting temperature in turn is dependent on the length of the oligonucleotide sequence and the composition of the DNA molecule. Generally, the annealing temperature should be no more than 5 degrees lower than the melting temperature of the primers. If the annealing temperature is set too low, the the result will feature partial annealing with mismatched bases that produces non-specific amplifications. If it is set too high, it will reduce the PCR yield.

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.

PCR focuses on a specific region of DNA defined by primers, whereas restriction enzymes focus on a specific sequence that may occur multiple times. Whereas PCR is used for DNA sequencing and gene isolation, restriction digests are used in cloning. Generally you would use PCR to multiply the number of sequences of interest to replicate and then use restriction digestion to prepare those sequences for cloning and to insert DNA fragments into plasmids.

4 How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning? The main consideration should focus on the overlap length the of the forward and reverse primers. The longer the insert and the larger the number of insert fragments, the longer the primers need to ensure stable annealing. According to a SnapGene video -for a single insert sequence of 0.1 to 0.5kb, the overlapping tails of between 15 to 30 nucleotides should be sufficient. For an insert sequence between 8 and 10 kb, the tail overlap should increase to about 40 bp in length.⁴

5 How does the plasmid DNA enter the E. coli cells during transformation? Plasmid DNA enters the E. coli cells by passing through temporary pores that appear in the bacteria when it is stressed (typically by heat)

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

6.1 Explain the other method in 5 – 7 sentences plus diagrams (either handmade or online). Golden Gate Assembly is a DNA assembly method that relies on Type IIS restriction enzymes such as Bsal that cleave DNA outside their recognition sequences. It has two steps that occur within the same reaction:

1. Type IIS restriction enzyme digestion
2. DNA ligation

Whereas Gibson Assembly relies on melting overlaps and filling gaps, Golden Gate uses Type IIS restriction enzymes to cleave outside their recognition sequences to produce unique overhangs. Those unique overhangs help make Golden Gate better able to assemble DNA that involves many many insert fragments.

These two diagrams borrowed from the SnapGene web site best describe the key parts of this approach.⁵

6.2 Model this assembly method with Benchling or Asimov Kernel!

Week 7 Genetic Circuits Part II: Neuromorphic Circuits

Genetic Circuits Part II: Neuromorphic Circuits

Part 1: Intracellular Artificial Neural Networks (IANNs)

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

Traditional genetic circuits tend to have a more restricted set of states they can use to respond to stimuli. IANNs support analog signal processing, which allows them to continuously adapt and respond to varying conditions instead of set of those that only register as ‘1’s or ‘0’s. This property can make them better suited to cope with the environmental bioprocesses that can exhibit different metabolic properties depending on their location in space and time ¹. IANNs are also better able to respond to noise in signals than traditional genetic circuits.

I think it would be interesting to create an IANN to support people who have hemochromatosis, a disorder that causes the body to absorb too much iron. An IANN would rely on measuring a analogue signnal of free iron concentration. The processing part of the circuit would involve summing the input signal of Fe concentration to detect when it passed a certain level. When the threshold was reached, the circuit could encourage further ferritin synthesis, perhaps such that the amount produced matched the amount of free iron.

I don’t know if it’s a weakness but I’d wonder how long engineered cells would last in the body. I’d also wonder whether parts of the circuit would have a risk of changing - for example somehow as the circuit ages, the threshold of when it starts producing ferritin gets lower and lower (producing too much response) or higher and higher (triggering too little response)

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.

Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2. I’m having trouble understanding this and spent half an hour on it before giving up to get more help on it. I understand that the perceptron f1 would take as input the x1 - the DNA encoding for the Csy4 endoribonuclease and something else that could turn that ‘on’ or ‘off’. That output would then feed into the diagram that’s shown, and the presence of endoribonuclease would turn ‘off’ the production of fluorescent proteins. Beyond that I’m not sure.

Part II

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

Mycelium (the vegetative part of fungi) can be used to produce insulation, packaging, or construction materials. Mycelium is also used to make a leather-like substance to make fashion items ². Research has also found that mycelium can be made to create a self-repairing fabric.³

Mycelium material can provide a biodegradable alternative to synthetic materials.⁴ There are claims that it can be more environmentally friendly, but I think the costs of growing it and reusing it at large scale need to happen before I can evaluate that claim.

The disadvantages of mycelium materials is that growing it for large-scale activities may be challenging. Its properties may not be as consistent as those of synthetic materials and its biodegradability means it would likely not be as durable.⁵

Also note fungal materials are also used to detect food pathogens, provide antibiotics, and alternative food materials to meat.

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?

Fungi can be genetically engineered to support bioremediation applications (e.g. sequestering heavy metals).⁶ For example, Jo reports that:

"Compared with bacteria, which are widely deployed for    bioremediation and typically require a continuous water phase to grow, filamentous fungi can extend their mycelia across the air-liquid interface into air-filled pores in the soil. Fungal mycelia can thus reach areas inaccessible to bacterial biofilms and form bridges transporting nutrients and chemicals across discontinuous microhabitats. In these environments, fungal cells absorb molecules from enzymatic degradation together with other chemicals, such as heavy metals and metalloids, and convert them into less toxic forms intracellularly."

Week 9: Cell free systems

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. The greatest advantage the cell-free protein synthesis has over in vivo methods is that the viability of the cell does not have to be maintained in order to maintain the viability of the protein synthesis. By allowing proteins to be grown in an open environment, greater control can be exercised over the key factors that produce the protein. Materials can be added to help protein production which may otherwise be toxic to cell populations. Scientists can control ion concentrations, cofactors and energy sources. Enhancing and prohibiting materials can also be added. Growing proteins in a cell-free environment also means protein production does not have to accommodate cloning of cells.

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

The cell extract. The extract contains the key components required for transcription and translation and include:

• ribosomes for protein synthesis
• tRNAs (which helps ensure the right amino acid is inserted into the protein being synthesised) for amino acid delivery
• Aminoacyl-tRNA synthetases (charging tRNAs)
• Initiation, elongation, and termination factors.
• Sometimes endogenous RNA polymerases, which help synthesise complementary RNAs from existing RNA templates. Gent

DNA or mRNA template. These encode the proteins of interest. The DNA templates will require transcription through the use of RNA polymerase. An mRNA template does not need to be transcribed and only needs to be translated. Both templates are used to help guide the process of producing amino acids.

Energy source. This is needed to power protein synthesis and generally comes from ATP and GTP Amino acids. These provide the raw materials to help build proteins. Nucleotides. These are needed if protein synthesis begins with DNA rather than mRNA. Buffers and salts.

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.

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

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.