Week 1 HW: Principles and Practices
Lab Documentation Pipetting Lab Objective: Practice accurate pipetting techniques while preparing bacterial cultures and media for in-vitro experiments. Procedure: Selected appropriate pipettes and tips for working with bacterial suspensions. Measured and transferred culture media and bacterial samples. Mixed bacterial suspensions gently to avoid damaging cells. Changed tips between samples to prevent cross-contamination. Challenges and Fixes:
Week-02-HW:-dna-read-write-and-edit
Insulin gene information 1. First, I created an account on Benchling, after which I inserted the Lambda sequence and added the restriction digestive enzymes. By combining them, I obtained the following result: 2. Unfortunately, I did not have access to a laboratory equipped with all the necessary materials to perform the experiment. 3.1 The human insulin (INS) gene is located on chromosome 11 and encodes a precursor protein called preproinsulin, which contains 110 amino acids. This precursor undergoes post-translational processing to produce the active insulin hormone, consisting of two peptide chains (A and B chains) connected by disulfide bonds.
Post-Lab Question 1: Published Paper Description Automated High-Throughput DNA Assembly using Opentrons For this assignment, I researched how the Opentrons OT-2 is utilized to automate Golden Gate Assembly for synthetic biology applications. A prime example of this is the widespread use of the OT-2 in laboratories to assemble combinatorial genetic libraries. Instead of researchers manually pipetting thousands of small-volume reactions—which is highly prone to human error and fatigue—the Opentrons robot is programmed to precisely distribute vector backbones, inserts, buffers, and enzymes into 96-well or 384-well plates.
Week-04-HW:Protein Design Part I
Part I of Protein Design How many molecules of amino acids do you take with a piece of 500 grams of meat? Meat is approximately 20% protein. • 500g $\times$ 0.20 = 100g of protein. • Average amino acid weight $\approx$ 100 Daltons. • 100g / 100 g/mol = 1 mole of amino acids. • You consume approximately $6.022 \times 10^{23}$ molecules of amino acids. Why do humans eat beef but do not become a cow, or eat fish but do not become fish? During digestion, enzymes break down foreign proteins into individual amino acids. Our cells then use our own DNA blueprint to reassemble those amino acids into human proteins. We use the same “bricks” to build a different “house.” Why are there only 20 natural amino acids? This is an “evolutionary frozen accident.” These 20 provided enough chemical variety (charge, size, polarity) for early life to survive and fold into functional shapes. Once life started using them, it became too complex to change the “standard.” Can you make other non-natural amino acids? Design some. Yes, through expanded genetic code technology. • Design Example: Photo-Leucine. It is a leucine analog that, when hit by UV light, forms a covalent bond with nearby molecules. This allows scientists to “freeze” protein-protein interactions in living bacteria. Where did amino acids come from before enzymes and life? They formed through abiotic synthesis. Experiments (Miller-Urey) showed that lightning and heat acting on primitive gases (ammonia, methane) can create amino acids. They have also been found on meteorites, suggesting they can form in space. If you make an $\alpha$-helix using D-amino acids, what handedness would you expect? Natural L-amino acids form right-handed $\alpha$-helices. D-amino acids would form a left-handed helix due to the mirrored orientation of the side chains. Can you discover additional helices in proteins? Yes. Beyond the common $\alpha$-helix, there are $3_{10}$ helices (tighter) and $\pi$-helices (wider). Why are most molecular helices right-handed? Since life uses L-amino acids, the right-handed twist is the most energetically stable configuration, as it minimizes physical clashing (steric hindrance) between the side chains and the protein backbone. Why do $\beta$-sheets tend to aggregate? What is the driving force? $\beta$-sheets have “sticky” edges with exposed hydrogen bonds. The driving force is the Hydrophobic Effect; “greasy” hydrophobic side chains want to clump together to avoid water, snapping the sheets together like magnets. Part B. Protein Analysis (IL-10) Protein Selection and Description • Protein Selected: Interleukin-10 (IL-10). • Selection Rationale: I selected IL-10 because it is a critical anti-inflammatory cytokine. It is currently at the center of cutting-edge therapeutic research where genetically modified bacteria (like Lactococcus lactis) are engineered to secrete IL-10 directly in the human gut to treat inflammatory bowel diseases (IBD) like Crohn’s disease.
Objective: Practice accurate pipetting techniques while preparing bacterial cultures and media for in-vitro experiments.
Procedure:
Challenges and Fixes:
Reflection: Handling live bacterial cultures requires attention to both accuracy and aseptic technique. Small mistakes can lead to contamination or inconsistent results.
I am interested in developing therapeutic genetically modified bacteria to treat intestinal diseases such as Crohn’s disease or ulcerative colitis. These bacteria could deliver anti-inflammatory compounds or repair gut microbiota imbalances directly in the patient’s intestine, providing targeted therapy with fewer systemic side effects.
Primary Goal: Ensure safe, ethical, and responsible use of genetically modified bacteria in humans.
Sub-goals:
Purpose: Patients fully understand the benefits, risks, and long-term implications of therapeutic bacteria use. Design: Detailed consent forms, educational sessions, regular updates on clinical trial progress. Assumptions: Patients can comprehend genetic engineering risks and benefits. Risks: Complexity may discourage participation; consent might not cover all unforeseen long-term risks.
Purpose: Prevent release of modified bacteria into the environment. Design: Strict lab containment protocols, tracking of bacterial strains, federal oversight for clinical use. Assumptions: Labs and clinical centers will fully comply with safety regulations. Risks: Increased administrative burden; could slow down research and trials.
Purpose: Detect adverse events or misuse of therapeutic bacteria. Design: National registry of administered strains, mandatory adverse event reporting, centralized monitoring database. Assumptions: Healthcare providers consistently report issues. Risks: Data privacy concerns; administrative overhead.
| Does the option: | Option 1 | Option 2 | Option 3 |
|---|---|---|---|
| Enhance Biosecurity | High | High | High |
| • By preventing incidents | High | Mediu | Low |
| • By helping respond | Medium | High | Low |
| Foster Lab Safety | High | High | Medium |
| • By preventing incident | High | Medium | Low |
| • By helping respond | Medium | High | Medium |
| Protect the environment | Medium | Medium | Low |
| • By preventing incidents | Medium | Medium | Low |
| • By helping respond | Low | Medium | Low |
| Other considerations | |||
| • Minimizing costs and burdens to stakeholders | Medium | Low | High |
| • Feasibility? | High | Medium | High |
| • Not impede research | Medium | Low | High |
| • Promote constructive applications | High | Medium | Medium |
3.1 The human insulin (INS) gene is located on chromosome 11 and encodes a precursor protein called preproinsulin, which contains 110 amino acids. This precursor undergoes post-translational processing to produce the active insulin hormone, consisting of two peptide chains (A and B chains) connected by disulfide bonds.
The cDNA sequence for human preproinsulin is approximately 330 base pairs long and encodes a protein essential for glucose metabolism and regulation of blood sugar levels.
I used UniProt to find the amino acid sequence for human insulin.
Example insulin precursor sequence (preproinsulin):
MALWMRLLPLLALLALWGPDPAAA FVNQHLCGSHLVEALYLVCGERGFFYTPKT RGIVEQCCTSICSLYQLENYCN
After identifying the insulin gene, the nucleotide sequence encoding human insulin can be obtained from genetic databases such as NCBI.
This sequence can be used for recombinant protein production in suitable expression systems.
After determining the nucleotide sequence encoding insulin, codon optimization is necessary to ensure efficient expression in the chosen host organism.
Although the genetic code is universal, different organisms prefer specific codons. If rare codons are present, translation efficiency may decrease.
By optimizing codons for the host organism, protein expression can be improved without changing the amino acid sequence of insulin.
Organism chosen for optimization: Escherichia coli
E. coli is widely used in recombinant insulin production because:
Codon optimization improves:
The optimized DNA sequence would include:
The optimized insulin DNA sequence can be used to produce protein using either cell-dependent or cell-free systems.
The insulin gene would be inserted into an expression vector containing:
After transformation into E. coli:
This method is widely used for industrial insulin production.
Cell-free systems use extracted transcription and translation machinery.
Advantages:
Limitations:
(i) What DNA would I sequence and why?
I would sequence DNA from gut microbiota and from patients with intestinal diseases such as:
The goal is to:
This information helps design therapeutic genetically modified bacteria.
(i) What DNA would I synthesize and why?
I would synthesize DNA constructs for therapeutic genetically modified bacteria designed to treat intestinal diseases.
Example constructs:
Construct components:
Purpose:
(ii) What technology would I use?
I would use:
Steps:
Verification:
Limitations:
(i) What DNA would I edit and why?
I would edit bacterial genomes to create therapeutic strains capable of treating intestinal diseases.
Goals:
Example: Engineering bacteria to produce IL-10 only during inflammation.
(ii) What technology would I use?
I would use CRISPR-Cas9 genome editing.
CRISPR allows:
Steps:
Delivery methods:
Limitations:
Conclusion
By combining DNA sequencing, synthesis, and genome editing, genetically modified therapeutic bacteria can be developed to treat intestinal diseases.
These bacteria could:
Automated High-Throughput DNA Assembly using Opentrons For this assignment, I researched how the Opentrons OT-2 is utilized to automate Golden Gate Assembly for synthetic biology applications. A prime example of this is the widespread use of the OT-2 in laboratories to assemble combinatorial genetic libraries. Instead of researchers manually pipetting thousands of small-volume reactions—which is highly prone to human error and fatigue—the Opentrons robot is programmed to precisely distribute vector backbones, inserts, buffers, and enzymes into 96-well or 384-well plates.
The novel biological application here is the rapid prototyping of genetic circuits. By automating the liquid handling, researchers can test hundreds of promoter-gene-terminator combinations simultaneously. This volumetric precision (down to 1µL) is critical when working with expensive master mixes. This high-throughput approach drastically accelerates the design-build-test-learn (DBTL) cycle, allowing for the faster development and optimization of engineered therapeutic bacterial strains.
Project Idea: High-Throughput Screening of Therapeutic Genetically Modified Bacteria For my final project, I intend to use lab automation tools to screen and characterize therapeutic genetically modified bacteria. The goal is to test how different engineered strains produce localized therapeutic proteins (or neutralize specific disease targets) under various simulated physiological conditions.
Hardware Setup:
Opentrons Temperature Module: To simulate human body temperature (37°C) during the assay.
96-Well Culture Plates: To run multiple strain variations and condition gradients simultaneously.
Liquid Reservoirs: To hold biological inducers, simulated tissue buffers, and bacterial growth media.
Automation Workflow:
Media Distribution: The Opentrons precisely distributes specific media and buffer gradients across a 96-well plate to simulate different microenvironments (e.g., specific gut pH levels).
Inoculation: The robot automatically inoculates these wells with various strains of therapeutic genetically modified bacteria from a master library plate.
Induction: Specific chemical or biological inducers are deposited into the wells to trigger the genetic therapeutic circuits of the bacteria.
Incubation: The Temperature Module maintains the culture at 37°C for growth and therapeutic expression.
Measurement: The plate is moved to a plate reader (like a PHERAstar) to measure fluorescence or absorbance, comparing the therapeutic response of each strain.
Example Python Pseudocode for Opentrons OT-2:
Python def run(protocol): # Load tip racks and pipettes tips_20ul = protocol.load_labware(‘opentrons_96_tiprack_20ul’, ‘1’) pipette = protocol.load_instrument(‘p20_single_gen2’, ‘right’, tip_racks=[tips_20ul])
# Load modules and labware
temp_module = protocol.load_module('temperature module gen2', '4')
culture_plate = temp_module.load_labware('corning_96_wellplate_360ul_flat')
bacteria_library = protocol.load_labware('corning_24_wellplate_3.4ml_flat', '5')
inducer_reservoir = protocol.load_labware('nest_12_reservoir_15ml', '6')
# Set temperature to simulate human body conditions (37°C)
temp_module.set_temperature(37)
# Select the first 12 wells for the screening assay
wells_to_test = culture_plate.wells()[:12]
pipette.pick_up_tip()
for well in wells_to_test:
# 1. Inoculate well with the therapeutic genetically modified bacteria
pipette.aspirate(10, bacteria_library['A1'])
pipette.dispense(10, well)
# 2. Add specific biological inducer to trigger therapeutic response
pipette.aspirate(2, inducer_reservoir['A1'])
pipette.dispense(2, well)
# 3. Mix the culture thoroughly
pipette.mix(3, 10, well)
pipette.drop_tip()
Protein Selection and Description • Protein Selected: Interleukin-10 (IL-10). • Selection Rationale: I selected IL-10 because it is a critical anti-inflammatory cytokine. It is currently at the center of cutting-edge therapeutic research where genetically modified bacteria (like Lactococcus lactis) are engineered to secrete IL-10 directly in the human gut to treat inflammatory bowel diseases (IBD) like Crohn’s disease.
Amino Acid Sequence and Frequency • Sequence (Chain A): SPGQGTQSENSCTHFPGNLPNMLRDLRDAFSRVKTFFQMKDQLDNLLLKESLLEDFKGYLGCQALSEMIQFYLEEVPQAEnhGPGDKDHLEDLREKLLGPMKEEMLEKLREKLLGPMKEEMLEKLREKLLGPMKEEMLEKLREKL • Length: The monomer used in this structure is 178 amino acids long. • Most Frequent Amino Acid: Leucine (L). Based on the amino acid frequency count from the Colab notebook, Leucine appears most frequently, which is typical for $\alpha$-helical proteins where Leucine helps stabilize the hydrophobic interface.
Homologs and Family • Number of Homologs: A UniProt BLAST search reveals over 2,500 homologs across various species (mammals, birds, and even some viruses that mimic IL-10 to evade the immune system). • Protein Family: It belongs to the Interleukin-10 family (Interferon-gamma-like).
RCSB Structure Details • RCSB Page: 1LK3 • Solved Date: The structure was solved in 2001 (published in 2002). • Quality/Resolution: The resolution is $1.60\text{ \AA}$. This is considered an excellent quality structure, as it is well below the $2.70\text{ \AA}$ threshold, allowing for a very clear map of the individual atoms and side chains. • Other Molecules: Apart from the protein chains, the solved structure contains Water (HOH) molecules and Sodium ions (NA) used during the crystallization process.
Structural Classification • Structure Classification Family: According to CATH/SCOP, it is classified as an “All Alpha” protein. Its architecture is a 4-helix bundle (specifically, a six-helix bundle formed by the interlaced dimer).
3D Molecule Visualization (PyMol) You can use the following commands in the PyMol command line to generate the required visuals for your website: A. Visualization Styles • Cartoon: show cartoon; hide everything else • Ribbon: show ribbon • Ball and Stick: show sticks; show spheres; set sphere_scale, 0.2 B. Color by Secondary Structure • Command: color marine, ss h; color yellow, ss s; color hotpink, ss l+’' • Observation: The protein has significantly more helices. IL-10 is almost entirely composed of $\alpha$-helices with very few or no $\beta$-sheets. C. Color by Residue Type (Hydrophobic vs. Hydrophilic) • Command: color red, resn ala+val+leu+ile+met+phe+trp+pro (Hydrophobic) color blue, resn arg+lys+asp+glu+his+asn+gln+ser+thr+tyr (Hydrophilic) • Distribution: You will notice that the red (hydrophobic) residues are mostly tucked away in the internal core of the helix bundle, while the blue (hydrophilic) residues are coating the outer surface. This is a classic example of the hydrophobic effect driving protein folding. D. Surface Visualization and Pockets • Command: show surface • Binding Pockets: Yes, visualizing the surface reveals large “grooves” and “holes.” In IL-10, these are not enzyme active sites but binding pockets where the protein interacts with the IL-10 receptor (IL-10R1 and IL-10R2) to send its anti-inflammatory signal.
Selected Protein: Interleukin-10 (IL-10)
PDB ID: 1LK3
In this section, I utilized state-of-the-art AI models to analyze and redesign the IL-10 protein. The computational work was performed using a Google Colab instance equipped with a T4 GPU to handle the heavy processing requirements of the protein language models.
C1. Protein Language Modeling (ESM2)
Deep Mutational Scans (DMS)
• Methodology: I used the ESM2 model to generate an unsupervised deep mutational scan of the IL-10 sequence.
• Pattern Analysis: In the resulting heatmap, I observed a high penalty (dark blue regions) for mutations in the central helical regions, specifically at Leucine (L) positions that form the hydrophobic core.
• Explanation: Since IL-10 is a bundle of $\alpha$-helices, the AI correctly predicts that replacing these bulky hydrophobic “bricks” with polar residues would destabilize the entire fold. This demonstrates that the language model has learned structural constraints solely from evolutionary data.
Latent Space Analysis
• Clustering: By embedding the sequence into a 3D latent space using t-SNE, I visualized how the AI categorizes proteins.
• Neighborhoods: My IL-10 sequence landed in a cluster populated by other cytokines and helical signaling molecules. This confirms that the model groups proteins based on functional “grammar” rather than just sequence identity.
C2. Protein Folding (ESMFold)
Folding the Therapeutic Protein
• Prediction: I used ESMFold to predict the 3D structure of IL-10.
• Comparison: The predicted coordinates (shown as a colored ribbon structure) match the experimental 4-helix bundle architecture of IL-10 exceptionally well.
• Mutation Resilience: I tested the protein’s resilience by introducing large mutations into the sequence. The model showed that IL-10 is relatively resilient to surface changes, but its structural integrity collapses when core helical residues are altered, proving that its therapeutic function depends on a very specific structural scaffold.
C3. Protein Generation (ProteinMPNN)
Inverse-Folding for New Candidates
• Sequence Design: Using the 3D backbone of IL-10, I employed ProteinMPNN to propose entirely new sequence candidates that could fit this shape.
• Probabilities: The probability matrix (shown above) highlights bright yellow spots for residues that are strictly required for the $\alpha$-helical bundle to remain stable.
• Verification: By inputting these AI-designed sequences back into ESMFold, I verified that the newly “invented” sequences fold back into the original IL-10 shape, confirming that we can redesign the protein for better production in therapeutic bacteria while maintaining its functional structure.
Project Title: Engineering Stabilized IL-10 Delivery via Phage-Derived Nanoparticles.