Hello! I’m Andrea 🌸, a 3rd-year Biotechnology student in Timișoara, Romania. I’m passionate about making life healthier 🌱, protecting the environment 🌍, caring for animals 🐾, and improving well-being for all humans 💖.
I would love to develop projects that enhance health 🧬, bring innovation 💡, make life easier ⚡, and spread happiness 😊. I also enjoy working with natural products and sustainable solutions 🍯🌿, because I believe in science that respects nature 🌷.
In this course, I look forward to learning more about molecular biology 🧬 and discovering what we can create — and how we can edit (almost) anything 😏.
Step 1. First, describe a biological engineering application or tool you want to develop and why. Something we are interested in is reading and biology, and we wanted to find a way to combine these two interests. We thought about creating bioluminescent bookmarks. These bookmarks would produce light naturally, because of the engineered microorganisms or biological materials that glow in different colors, without the need for batteries, which makes them environmentally friendly.
✨ Week 3 - homework ✨ Here is the link to my Automation Art (2026 HTGAA Bacteriophage): https://opentrons-art.rcdonovan.com/?id=jy86j81azdyuadc After generating this bacteriophage design in Opentrons Art, I created a copy of the Colab notebook and worked there to build a Python protocol that would allow the Opentrons robot to reproduce the artwork on a plate. Since I don’t know Python, I first used ChatGPT to generate the code, but the initial version contained many errors when running in Colab. I then switched to Gemini, which helped me debug and fix the issues. I manually entered the coordinates from the link above, step by step, to reconstruct the design inside the protocol. After completing the process, I obtained the following image.
✨ 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)
Using an online converter ( https://www.unitconverters.net/weight-and-mass/gram-to-dalton.htm ), I calculated that 100 Daltons (1 amino acid) corresponds to approximately 1.66 × 10⁻²² g. After dividing the mass of a 500 g piece of meat by this value, I found the total number of amino acid molecules:
Subsections of Homework
Week 1 HW: Principles and Practices
Step 1. First, describe a biological engineering application or tool you want to develop and why.
Something we are interested in is reading and biology, and we wanted to find a way to combine these two interests. We thought about creating bioluminescent bookmarks. These bookmarks would produce light naturally, because of the engineered microorganisms or biological materials that glow in different colors, without the need for batteries, which makes them environmentally friendly.
Other motivations are to add a little more aesthetic while reading and to explore safe applications of bioluminescence outside the lab.
AI generated
Step 2. 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.
The main ethical goal would be to reduce environmental impact by avoiding traditional batteries, while making sure the user is safe and minimizing risks from the materials used in the bookmark (plastic or glass). This big goal can be break down into:
For environmental protection, the bookmarks should be made from biodegradable materials so that they help reduce plastic waste and are less harmful to the environment.
To make sure users are safe, the bioluminescent organisms used in the bookmarks need to be tested carefully, and if necessary, they could be genetically modified to prevent them from having any harmful or toxic genes.
Step 3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”).
Purpose: Right now, light-up bookmarks exist, but they use batteries that can damage the environment. We want to replace them with bookmarks that produce light without harmful batteries.
Design: We thought to make the bookmark thin enough so that the book can close properly, while still being made from a biodegradable material that is resistant to falling or damage. Inside the bookmark, bioluminescent organisms would be placed to provide light without using batteries.
Assumptions: The autonomy of the bookmark depends on how long the bioluminescent organism can live, which might be a relatively short time. It is also assumed that the production costs could be high, making the bookmark expensive to produce and buy, even though it might only be used for a limited period of time.
Risks of Failure: The bioluminescence may fail due to lack of oxygen, poor organism survival, or degradation of the material.
Success: The bookmark is visually appealing, functional, and draws attention while being eco-friendly, providing a safer alternative to battery-powered bookmarks.
Step 4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:
Does the option:
Purpose
Design
Assumptions
Enhance biosecurity
1. Preventing incidents
2
2
3
2. Helping respond
3
3
3
Foster lab safety
1. Improving procedures
2
1
2
2. Encouraging safe behavior
2
2
2
Protect the environment
1. Reducing waste
1
1
2
2. Designing safer materials
1
1
2
Other considerations
1. Cost & feasibility
2
2
3
2. Social impact
1
1
2
🌟 Short explanation of the scoring choices
I gave the highest scores to environmental protection because the bookmarks are made from biodegradable materials that reduce waste. Lab safety scored well since testing the organisms helps prevent accidents. Biosecurity scored lower because this project isn’t focused on biological risks. Cost and social impact were moderate: the bookmarks might be a bit expensive, but they are attractive and eco-friendly. Overall, the scores reflect a balance between safety, environmental benefits, and practicality.
Step 5. 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.
I would prioritize a combination of the bookmark’s aesthetic appeal and environmental safety. A visually attractive bookmark will draw attention and encourage people to buy and use it. At the same time, using biodegradable materials and safe bioluminescent organisms helps protect the environment and ensures user safety.
The main trade-off is cost and production complexity. Making a thin, eco-friendly, and visually appealing bookmark with tested organisms could be more expensive and harder to produce. Another assumption is that the organisms will live long enough to provide visible light, but their lifespan might be limited.
I would recommend this approach to companies that make educational or novelty products and to environmental regulators, because they can make sure the product is safe, sustainable, and still attractive.
I simulated a restriction digest on λ‑DNA (E. coli bacteriophage) in Benchling using several restriction enzymes: EcoRI, HindIII, BamHI, KpnI, EcoRV, SacI, and SalI. Each enzyme recognizes its own specific DNA sequence, producing different fragment patterns depending on how many cut sites are present. Some enzymes generate sticky ends, while others like EcoRV create blunt ends. By comparing the band patterns, we can see which enzymes cut the DNA, how many fragments they produce, and estimate fragment sizes—from large fragments (~10 kb) to very small ones (~100 bp). If an enzyme doesn’t cut, the result is a single intact band.
✨ Part 3: DNA Design Challenge ✨
3.1. Choose your protein
For this assignment, I chose linalool synthase, an enzyme involved in the biosynthesis of linalool, one of the major aromatic and bioactive compounds found in lavender (Lavandula spp.). Since my bachelor’s thesis focuses on the bioactive components of lavender, including linalool, this protein felt like a meaningful and relevant choice.
Using UniProt, I obtained the amino acid sequence for the linalool synthase I selected:
3.2. Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence
Using the online tool available at https://proteiniq.io , I reverse‑translated the amino acid sequence of the linalool synthase protein into a corresponding DNA sequence. This process is based on the Central Dogma of Molecular Biology, which states that DNA makes RNA, and RNA makes protein. By working backwards from the protein sequence, the tool generates a plausible nucleotide sequence that could encode the same protein.
I obtained the following reverse‑translated DNA sequence:
Different organisms prefer different codons for the same amino acid. When a gene from one species is expressed in another, the codon usage may not match the host’s preferences, which can slow down translation and reduce protein expression.
Codon optimization rewrites the DNA sequence using the codons most frequently used by the host organism, without changing the amino acid sequence. This increases translation efficiency, mRNA stability, and overall protein yield. It is a standard technique in biotechnology to improve recombinant protein production.
2. Which organism did you choose and why?
I chose Nicotiana tabacum for codon optimization because it is a plant species, just like lavender, the natural source of linalool synthase. Since both are plants, their codon usage patterns are more similar, making N. tabacum a more suitable host for expressing a plant-derived enzyme than bacteria or yeast.
In addition, several studies on related Nicotiana species show that linalool plays an important ecological role in plant defense. For example, Nicotiana attenuata emits (S)-(+)-linalool to attract predators of herbivores such as Manduca sexta, reducing leaf damage. Linalool is also known to have insecticidal and repellent properties in many species, including mosquitoes and agricultural pests.
Because N. tabacum is widely used in biotechnology, cosmetics, and pharmaceutical production, enhancing its natural protection against insects through increased linalool production could reduce the need for pesticides. Introducing a codon‑optimized linalool synthase gene from lavender into tobacco could therefore help the plant produce higher levels of linalool and benefit from its natural repellent and defensive properties.
Codon Optimization Using Two Different Tools
To ensure that my codon optimization results were reliable and not dependent on a single algorithm, I performed the optimization using two independent online tools, each of which uses different reference datasets and calculation methods for Nicotiana tabacum. Because of these differences, the CAI (Codon Adaptation Index) and GC% values vary slightly between platforms, which is expected.
This tool showed a clear improvement in CAI, indicating that the optimized sequence is much better adapted to the codon usage preferences of Nicotiana tabacum.
In this case, the original sequence was already well adapted according to NovoPro’s reference dataset, and the optimized version produced a CAI of similar magnitude, with a slightly higher GC%.
3.4. You have a sequence! Now what?
Now that I have my optimized DNA sequence, the next step is to think about how this DNA could actually be used to produce the protein. In general, the process is the same in any biological system: the DNA is transcribed into mRNA, and then the mRNA is translated by ribosomes into the protein.
One way to do this is by expressing the gene directly in a plant, such as Nicotiana tabacum . To get the gene into the plant genome, a genome‑editing tool like CRISPR–Cas9 could be used. Cas9 can make a cut at a specific location in the plant’s DNA, and then my optimized gene can be inserted at that site. After the gene is integrated, the plant’s own machinery will read it, make mRNA from it, and then produce the protein.
Another option would be to use cell‑free expression systems or express the gene in E. coli, for example, but plant expression is especially relevant when the protein is naturally part of a plant pathway.
Overall, once the DNA is inside the host (either a cell or a cell‑free system), the basic flow is the same:
DNA → mRNA → protein.
DNA
→
RNA
→
Protein
3.5. [Optional] How does it work in nature/biological systems?
1. Describe how a single gene codes for multiple proteins at the transcriptional level.
A gene is first transcribed into a long RNA molecule called pre‑mRNA. This pre‑mRNA contains exons, which are kept and introns which will be removed.
Alternative Splicing The cell can splice (cut and join) the exons in different combinations. Different exon combinations = different mRNA molecules.
Different mRNAs → different proteins Each mRNA variant is translated into a protein. Because the exon order changes, the amino acid sequence changes too, and finally, one gene can produce multiple proteins.
DNA
→
EXON1 — intron — EXON2 — intron — EXON3
↓ alternative splicing
mRNA Variant 1
→
EXON1 + EXON2 + EXON3
mRNA Variant 2
→
EXON1 + EXON3
↓ translation
Protein 1
≠
Protein 2
2. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!!
In nature, DNA is double-stranded, but only one strand is used as the template during transcription. RNA polymerase reads the template strand (3′→5′) and synthesizes a single-stranded RNA molecule based on base complementarity: A pairs with U (because RNA has no T), T pairs with A, G with C, and C with G.
After transcription, the mRNA is read in groups of three nucleotides called codons. Each codon corresponds to one amino acid. During translation, tRNA molecules bring amino acids to the ribosome by matching their anticodon to each codon on the mRNA. As amino acids join together, they form the polypeptide chain — the protein.
DNA 5′→3′: ATG TCA ATA AAC ATA AAT
DNA 3′→5′: TAC AGT TAT TTG TAT TTA
RNA 5′→3′:AUGUCAAUAAACAUAAAU
AA:MSININ
✨ Part 4: Prepare a Twist DNA Synthesis Order ✨
I created my Twist Bioscience account, and I already had a Benchling account from the previous step. In Benchling, I created a new DNA sequence named Linalool_E.coli, where I inserted the codon‑optimized DNA sequence of my gene of interest (Linalool synthase), optimized for E. coli.
Before the coding sequence, I added the following genetic elements:
Promoter (BBa_J23106)
TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC
RBS (BBa_B0034 – ribosome binding site)
CATTAAAGAGGAGAAAGGTACC
Start Codon (ATG)
ATG
Coding Sequence (Linalool synthase, codon‑optimized for E. coli)
The resulting linear map can be seen in the first image, next to this textm and the second one is the visual diagram.
✨ This is the plasmid I just built! ✨
✨ Part 5: DNA Read/Write/Edit ✨
5.1 DNA Read
I would choose to sequence DNA from plants that naturally show resistance to agricultural pathogens, as well as DNA from the pathogens themselves. Understanding the genetic basis of plant immunity — for example, genes involved in pathogen recognition, antimicrobial compound production, or stress signaling — can help identify natural strategies that crops use to defend themselves without relying on chemical pesticides.
This connects directly with my own project, where I designed a plasmid for the biosynthesis of linalool in E. coli. Linalool is a naturally occurring monoterpene found in many aromatic plants, and it is known to have antimicrobial and insect‑repellent properties. By studying the DNA of pathogen‑resistant plants, we can discover how these organisms use compounds like linalool or related molecules as part of their defense systems.
Sequencing both plant and pathogen DNA would therefore support sustainable agriculture by revealing natural defense pathways that could be enhanced, transferred, or synthetically produced — reducing the need for synthetic insecticides and promoting more resilient crop systems.
Sequencing technology I would use
To sequence plant resistance genes and agricultural pathogens in a way that is fast, portable, and useful directly in the field, I would use Oxford Nanopore MinION. This technology allows rapid, on‑site DNA analysis, which supports sustainable agriculture by allowing early pathogen detection and reducing unnecessary pesticide use.
1. Generation
I would use Oxford Nanopore MinION, a third‑generation method.
It reads DNA directly by measuring small electrical changes as the strand passes through a nanopore.
2. Input & Preparation
Input: purified DNA from plant tissue or pathogens.
Preparation steps: • DNA extraction • Optional DNA cutting/fragmentation • Add Nanopore adapters • Load the sample into the MinION • Sequencing starts
3. How it reads the DNA
The DNA strand moves through a nanopore.
Each base changes the electrical signal slightly.
The device reads these signal patterns and the software turns them into A, T, C, or G.
4. Output
The output is the actual DNA reading: long sequences + quality scores (FASTQ files).
This shows exactly which bases were detected in the sample.
5.2 DNA Write
I would like to synthesize the DNA for a plant gene that naturally produces linalool, a fragrant molecule with mild antimicrobial and insect‑repellent properties. Adding this gene to Nicotiana tabacum could help the plant protect itself better in a natural way, supporting more sustainable agriculture without relying on chemical pesticides.
What are the essential steps of your chosen sequencing methods?
I would use commercial DNA synthesis technology, such as the automated chemical DNA writing used by companies like Twist Bioscience. This method can quickly and accurately produce the exact DNA sequence I want, including the gene responsible for linalool production. It is reliable, fast, and ideal for creating small custom DNA fragments for research.
What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability?
The main limitations of DNA synthesis are related to length, errors, time, and cost.
Longer DNA sequences are harder to synthesize and may take more time to produce. Errors can appear during synthesis, so the final DNA often needs to be checked. The process can also take longer for complex or larger sequences. Finally, one of the biggest limitations is cost, because high‑quality synthesis technologies and equipment are expensive.
5.3 DNA Edit
If I could edit DNA, I would choose to modify the genome of common ragweed (Ambrosia artemisiifolia). I would edit the genes responsible for pollen development and allergenic pollen proteins. I would target two types of DNA regions.
First, I would edit genes controlling pollen formation so the plant becomes male-sterile and produces non-viable pollen. This could be done using modern gene-editing technologies such as CRISPR, which allow precise mutations in specific genes. If the pollen cannot develop properly, the plant would release little or no functional pollen, which would strongly reduce allergy problems and also limit the plant’s uncontrolled spread.
Second, I would consider modifying the DNA coding for the main allergenic pollen proteins, such as the Amb a allergens, so that their structure becomes less likely to trigger immune reactions. Even if allergies cannot be completely removed, reducing both pollen quantity and allergen strength could significantly decrease the public health impact.
AI generated
The reason I would edit ragweed DNA is that this plant already grows naturally in polluted and disturbed environments and tolerates poor soil conditions. Because of this, it could potentially be used for **opportunistic phytoremediation**, meaning helping absorb some heavy metals from contaminated soil without needing intensive cultivation. Currently, public policy focuses on **elimination**, not **domestication** of ragweed because of its allergy risk. However, if genetic editing reduced pollen hazards and spread, the plant might instead be safely managed and used in an **ecological direction** for environmental cleanup.
Therefore, editing ragweed DNA could transform a harmful invasive species into a controlled plant with potential environmental benefits while reducing risks to human health.
1. How does your technology of choice edit DNA? What are the essential steps?
A: The DNA editing technology I would use for both approaches is CRISPR. For the first, CRISPR would target pollen-development genes by designing guide RNA, cutting the DNA with Cas9, and letting the cell repair it to create non-viable pollen, while for the second, CRISPR would target allergen genes, cut the DNA at epitope regions, and use a repair template to introduce small changes so the protein becomes less allergenic.
2. What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?
A: The DNA editing technology I would use for both approaches is CRISPR. For the first, I would prepare a guide RNA targeting the pollen-development gene, and for the second, a guide RNA targeting the allergen gene along with a DNA template for small changes. The input for the editing includes the plant cells, the Cas9 enzyme, the guide RNAs, and the repair template (for the allergen modification), which together allow the plant to make the desired DNA changes.
3. What are the limitations of your editing methods (if any) in terms of efficiency or precision?
A: The main limitations of CRISPR are that DNA repair is not always perfect, which can cause unintended mutations, and not all cells may be successfully edited, so the efficiency is less than 100%. Precise changes, like modifying allergen proteins, are harder to achieve than simply turning a gene off, making the method less precise for complex edits.
1. What’s the most commonly used method for oligo synthesis currently?
Answer: The most commonly used method for oligo synthesis today is phosphoramidite DNA synthesis.
2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
Answer: It is difficult because each chemical synthesis step has less than 100% efficiency, errors accumulate with length, making oligos longer than ~200 nt unreliable.
3. Why can’t you make a 2000bp gene via direct oligo synthesis?
Answer: Chemical DNA synthesis has an error rate of about 1 in 100 bases, and the errors accumulate over 2000 bases, so it is impossible to obtain a correct full‑length gene through direct synthesis.
After generating this bacteriophage design in Opentrons Art, I created a copy of the Colab notebook and worked there to build a Python protocol that would allow the Opentrons robot to reproduce the artwork on a plate. Since I don’t know Python, I first used ChatGPT to generate the code, but the initial version contained many errors when running in Colab. I then switched to Gemini, which helped me debug and fix the issues.
I manually entered the coordinates from the link above, step by step, to reconstruct the design inside the protocol. After completing the process, I obtained the following image.
✨ Post-Lab Questions ✨
1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
Article Title: AssemblyTron: Flexible automation of DNA assembly with Opentrons OT‑2 lab robots
Authors: John A. Bryant Jr., Mason Kellinger, Cameron Longmire, Ryan Miller, R. Clay Wright
Year: 2022
DOI:https://doi.org/10.1101/2022.09.29.510219
Article 1
In the article “AssemblyTron: Flexible automation of DNA assembly with Opentrons OT‑2 lab robots,” the authors aim to demonstrate the value of using automation tools in scientific research. Their main objective is to show how the Opentrons OT‑2 platform can simplify laboratory workflows, reduce human error, and significantly shorten reaction setup time.
The study uses plasmid DNA, including E. coli plasmids carrying chromoprotein genes and plasmids encoding plant transcription factors. By working with these constructs, the authors illustrate several capabilities of the OT‑2 robot. For example, the system automatically prepares PCR reactions with optimized annealing temperatures, performs Golden Gate Assembly to build multi‑fragment plasmids with high accuracy, and executes homology‑based assembly methods such as AQUA and IVA for cloning and site‑directed mutagenesis.
Overall, the article highlights how integrating Opentrons automation into the Design‑Build‑Test‑Learn cycle can make molecular biology more efficient, reliable, and accessible to researchers.
Article 2
However, the second article, “Real‑time AI‑driven quality control for laboratory automation,” demonstrates that even though automation brings many advantages, systems like the Opentrons OT‑2 are not completely error‑free. The authors highlight that issues such as missing pipette tips, incorrect liquid volumes, or failed aspiration steps can still occur during automated workflows.
To address these limitations, the study introduces an AI‑based computer‑vision system capable of detecting such errors in real time. By integrating a YOLOv8 deep‑learning model with a camera mounted on the OT‑2, the system continuously monitors pipetting actions and alerts the user when something goes wrong.
This shows that while automation improves efficiency and reproducibility, additional quality‑control tools are essential to ensure reliability, especially in sensitive biological experiments.
2. Write a description about what you intend to do with automation tools for your final project.
For my final project, I want to explore how automation tools can support a workflow focused on improving plant-based strategies for reducing heavy metal contamination in soil. I’m also interested in how genetic modifications could help plants grow with fewer pesticides. To make the experimental steps more reliable and easier to repeat, I plan to automate several parts of the Design–Build–Test cycle.
I would use the Opentrons OT‑2 to automate tasks such as preparing PCR reactions, assembling genetic constructs, and setting up transformation mixes. Automating these steps would reduce pipetting errors and make it easier to test multiple gene variants in parallel. I may also design a 3D‑printed holder to keep plant DNA extraction tubes stable on the OT‑2 deck, since plant samples often come in irregular formats.
Here is an example of how part of the workflow could look in pseudocode:
for variant in gene_variants:
pipette.transfer(master_mix, PCR_well[variant])
pipette.transfer(template_DNA, PCR_well[variant])
pipette.transfer(primer_sets[variant], PCR_well[variant])
run_thermocycler(“PCR_program”)
Later, I could use Ginkgo Nebula to explore or simulate different gene designs related to metal uptake or pest resistance, helping me decide which constructs are worth testing. Overall, automation would make the workflow more efficient and reproducible, allowing me to focus more on analyzing plant performance rather than repeating manual prep steps.
✨ Final Project Ideas ✨
As explained in this week’s recitation, I created three slides in my Node’s section of the shared slide deck, each presenting a different idea for my Individual Final Project.
These ideas reflect different ways I could combine synthetic biology, environmental applications, and automation tools for my final project.
Week 4 — Protein Design Part I
✨ 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)
Using an online converter ( https://www.unitconverters.net/weight-and-mass/gram-to-dalton.htm ), I calculated that 100 Daltons (1 amino acid) corresponds to approximately 1.66 × 10⁻²² g. After dividing the mass of a 500 g piece of meat by this value, I found the total number of amino acid molecules:
500 g 1.66 × 10⁻²² g/molecule
≈ 3.01 × 10²⁴ molecules
This means there are ~ 3.01 × 10²⁴ amino acid molecules in 500 grams of meat.
2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?
Humans eat beef or fish, but we do not become cows or fish because each species has its own unique genome. Eating proteins from another species does not change our DNA; our body simply digests the proteins into amino acids and uses them to build its own proteins. We only take the building blocks, not the instructions for making another species.
3. Why are there only 20 natural amino acids?
There are only 20 natural amino acids because the genetic code in DNA and mRNA is built to encode only these 20. Although there are 64 codons, many codons code for the same amino acid (redundancy in the genetic code). Scientists are experimenting with creating non-natural amino acids to expand the range of possible proteins, but in nature, only 20 are used.
4. Can you make other non-natural amino acids? Design some new amino acids.
I’m not completely sure how it works, but I remember from George Church’s slides that scientists can create new non-natural nucleobases. I guess that by using these artificial bases in the genetic code, it might be possible to produce new non-natural amino acids, although I don’t know the exact method.
5. Where did amino acids come from before enzymes that make them, and before life started?
Based on the information I found in this article https://doi.org/10.1002/chem.202201419 , amino acids existed before life and before enzymes, formed through non-enzymatic chemistry. Enzymes appeared later as proteins that accelerated chemical reactions, including the synthesis of other proteins. Experimental evidence for prebiotic amino acid formation comes from the Miller–Urey experiment, from 1953, in which gases such as CH₄, NH₃, H₂O, and H₂ were exposed to electrical energy, producing amino acids. (https://www.britannica.com/science/Miller-Urey-experiment )
6. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?
D-amino acids (D = dextro, right) are enantiomers, meaning they are mirror versions of L-amino acids (the natural amino acids in proteins, L = levo, left). If you make an α-helix using D-amino acids, the helix will be left-handed. Even though L-amino acids are “left” in configuration, when they form a helix they twist to the right because this is the most stable arrangement for hydrogen bonds and steric interactions. D-amino acids are mirror images, so their α-helix twists in the opposite direction.
7. Can you discover additional helices in proteins?
I’m not sure, but I guess it might be possible to discover additional or unusual helices in proteins that we don’t know yet. Methods like X-ray crystallography or NMR might reveal new structures, but I don’t know the details.
8. Why most molecular helices are right-handed?
Most molecular helices are right-handed because the natural amino acids in proteins are L-amino acids (left-handed in configuration). When L-amino acids fold into a helix, the right-handed α-helix is the most stable arrangement due to optimal hydrogen bonding and minimal steric strain.
9. Why do β-sheets tend to aggregate?
-What is the driving force for β-sheet aggregation?
β-sheets tend to aggregate because their backbone groups (NH and CO) can form extensive hydrogen bonds with neighboring strands from other molecules. The main driving force for β-sheet aggregation is hydrogen bonding, together with hydrophobic interactions, which increase structural stability and lower the overall energy of the system.
AI generated
10. Why do many amyloid diseases form β-sheets?
-Can you use amyloid β-sheets as materials?
Many amyloid diseases form β-sheets because misfolded proteins adopt β-sheet–rich structures that can form extensive hydrogen bonds between different molecules. This leads to stable aggregates called amyloid fibrils, which accumulate in tissues and cause disease. Yes, amyloid β-sheets can be used as materials because they form highly stable and mechanically strong fibrils. Scientists are studying them as biomaterials for nanotechnology and medical applications.
11. Design a β-sheet motif that forms a well-ordered structure.
I am not completely sure how to design a specific β-sheet motif, but I would use amino acids that favor β-sheet formation, like valine and isoleucine. These amino acids are hydrophobic and have side chains that fit well in the extended β-strand structure, which helps the sheet stay stable. I would also alternate hydrophobic and polar residues, because in β-strands the side chains point up and down, so this pattern allows the sheet to interact with water on one side and form a stable hydrophobic core on the other. Together with hydrogen bonds between the strands, this could make a well-ordered and stable β-sheet.
✨ Part B. Protein Analysis and Visualization ✨
1. Briefly describe the protein you selected and why you selected it.
