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:
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 1. 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. 2. 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.”
Week-05-HW: Protein Design & Engineering Part II
Final Project Report: Computational Protein Engineering 🧬 PART A: Therapeutic Peptide Design for SOD1 (ALS) 1. Objective Amyotrophic Lateral Sclerosis (ALS) is often linked to mutations in the SOD1 protein, such as the A4V variant, which leads to toxic protein aggregation. Our goal was to design a synthetic peptide binder to act as a “molecular shield,” stabilizing the protein and preventing aggregation.
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 |
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()
1. How many molecules of amino acids do you take with a piece of 500 grams of meat? Meat is approximately 20% protein.
2. 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.”
3. 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.”
4. Can you make other non-natural amino acids? Design some. Yes, through expanded genetic code technology.
5. 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.
6. 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.
7. Can you discover additional helices in proteins? Yes. Beyond the common $\alpha$-helix, there are $3_{10}$ helices (tighter) and $\pi$-helices (wider).
8. 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.
9. 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.
1. Protein Selection and Description
2. Amino Acid Sequence and Frequency
3. Homologs and Family
4. RCSB Structure Details
5. Structural Classification
6. 3D Molecule Visualization (PyMol) You can use the following commands in the PyMol command line to generate the required visuals for your website:
show cartoon; hide everything else | show ribbon | show sticks; show spheres; set sphere_scale, 0.2color 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.color red, resn ala+val+leu+ile+met+phe+trp+pro (Hydrophobic) and 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.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.
Deep Mutational Scans (DMS)
Latent Space Analysis
Folding the Therapeutic Protein
Inverse-Folding for New Candidates
Project Title: Engineering Stabilized IL-10 Delivery via Phage-Derived Nanoparticles.
While genetically modified bacteria can produce IL-10 to treat intestinal inflammation, the protein itself is often unstable and degrades quickly due to the acidic and protease-rich environment of the human gut. For a therapeutic to be effective, the IL-10 molecule must maintain its specific $\alpha$-helical bundle shape until it reaches the target receptors.
We will use the same AI-driven design cycle tested in Part C to engineer a “Super-IL-10” with enhanced thermodynamic stability:
Input: IL-10 Wild-Type Sequence $\rightarrow$ ESM-2 Scan (Identify stability hotspots) $\rightarrow$ ProteinMPNN (Optimize core packing) $\rightarrow$ ESMFold (3D fold verification) $\rightarrow$ Output: Stabilized Therapeutic Candidate.
Amyotrophic Lateral Sclerosis (ALS) is often linked to mutations in the SOD1 protein, such as the A4V variant, which leads to toxic protein aggregation. Our goal was to design a synthetic peptide binder to act as a “molecular shield,” stabilizing the protein and preventing aggregation.
We employed AI-driven strategies (PepMLM and moPPIt) to generate peptide candidates. By applying targeted design to residues 1-6 near the mutation site, we optimized the binding affinity.
Using PeptiVerse, we validated the lead candidate for biological safety. The results confirm a high probability of success for therapeutic application.
Bacteriophages use the L-Protein to perforate the bacterial cell wall. E. coli develops resistance by mutating the DnaJ chaperone, which is required for the L-protein to fold. Our objective was to engineer L-protein mutants that gain structural autonomy.
We simulated the DnaJ (Chaperone) and L-Protein (Viral) complex using AlphaFold2 Multimer to identify stability gaps.
Sequence Coverage: The analysis shows that while DnaJ is highly conserved, the L-protein region has very low evolutionary coverage, highlighting its unique and unstable nature.
Structural Stability (pLDDT): The L-protein (Chain B) is consistently highlighted in red, indicating very low confidence (<50 pLDDT). This confirms the protein is intrinsically disordered and depends on DnaJ.
Interaction Matrix (PAE): The PAE plots confirm the specific interaction zones between the two chains. Lower error (blue) at the interface shows where the L-protein docks onto DnaJ.
Based on the structural vulnerabilities identified, I propose 5 mutations to enhance autonomous folding and lysis efficiency:
| # | Mutation | Region | Engineering Goal |
|---|---|---|---|
| 1 | L25P | Soluble | Increase structural autonomy (DnaJ independence). |
| 2 | R14G | Soluble | Stabilize the domain responsible for folding. |
| 3 | F52L | Transmembrane | Enhance membrane insertion for faster killing. |
| 4 | W45A | Transmembrane | Optimize pore stability in the bacterial wall. |
| 5 | S31A | Soluble | Stabilize the N-terminal soluble domain. |
A successful mutant is defined by its ability to maintain a high Plaque Forming Unit (PFU) count even in DnaJ-deficient E. coli. Computationally, success is marked by an increased pLDDT score, showing that the L-protein has shifted from a disordered state (red) to a structured one (blue/cyan).
This project successfully combined Targeted Peptide Design for neurodegeneration and Structural Engineering for phage therapy. By using AI to bypass natural chaperones and enhance binding affinity, we have developed a blueprint for next-generation precision medicine.