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 beloved wife suffers from cutaneous HPV virus (warts) that has keratinized and is very stubborn. She has lived with it for two decades and gone through several cryotherapy and laser therapy treatments with no results. I am appalled at the lack of treatments available for this virus. I would like to explore things we learn in the class to see if we can build something that can help people like her around the world. The subcutaneous nature of the virus makes it a good candidate for building something that doesnt have to be injested.

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?
6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?
7. Can you discover additional helices in proteins?
8. Why are most molecular helices right-handed?
9. Why do β-sheets tend to aggregate? What is the driving force for β-sheet aggregation?
10. Why do many amyloid diseases form β-sheets? Can you use amyloid β-sheets as materials?
11. Design a β-sheet motif that forms a well-ordered structure.

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project