Week 1 HW: Principles and Practices

🧬 Week 1 Homework Components

📋 Professor Questions & Answers

Detailed scientific answers to questions from:

• Professor Jacobson — DNA polymerase error rates, genetic code degeneracy
• Dr. LeProust — Oligonucleotide synthesis methods and limitations
• Professor Church — Essential amino acids and the “Lysine Contingency”

🔬 BioVolt Project - DIY Electroporation Device

Complete end-to-end project documentation including governance assessment and interactive Python application.

     /\_/\  
    ( o.o ) 
     > ^ <
    /|   |\
   (_|   |_)
   
   "Meow! Check out both sections above!"

BioVolt Governance Assessment — Policy Options Comparison - I filled this out anyways but the project is located in the Sub as the Cat suggested- Meow

The table below compares three governance approaches for the BioVolt DIY electroporation device across multiple criteria. Scoring: 3 = Best, 2 = Moderate, 1 = Worst.

Option Summaries

Option 1 — Community-Led Self-Governance:
✓ Best for: response capacity, feasibility, minimizing burdens, not impeding research
✗ Weaker on: prevention (relies on voluntary participation; rogue actors may ignore)

Option 2 — Targeted Product Restrictions (Safety Warnings/Labels):
✓ Best for: feasibility, moderate prevention without bans
✗ Weaker on: response capacity (warnings don’t help after incidents), limited impact on determined bad actors

Option 3 — Regulatory Classification (Licensing/HVA Review):
✓ Best for: prevention (permits, training, HVA peer review blocks worst misuse)
✗ Weaker on: costs, feasibility, impedes DIY research, harms global equity

Recommendation: Prioritize Option 1 (community self-governance) as primary, combine with Option 2 (warnings) as secondary safeguard. Avoid Option 3 unless clear evidence of high-risk proliferation emerges.

Week 2 HW: DNA Read, Write, & Edit

🧬 Week 2 Homework Components

DNA Read, Write, & Edit — sequencing and synthesis workflows, restriction digests and gel electrophoresis, genome-editing frameworks.

📋 Overview

This week covers:

• Part 0: Basics of Gel Electrophoresis
• Part 1: Benchling & In-silico Gel Art ✓
• Part 2: Gel Art — Restriction Digests and Gel Electrophoresis (wet lab, optional with lab access)
• Part 3: DNA Design Challenge ✓
• Part 4: Prepare a Twist DNA Synthesis Order ✓
• Part 5: DNA Read/Write/Edit ✓

Content to be added as you complete each part.

Week 3 HW: Lab Automation

🤖 Week 3 Homework: Lab Automation

Find and describe a published paper utilizing automation for novel biological applications; describe automation tools for your final project.

📋 Overview

     ╔═══════════════════════════════════════════════════════════════╗
     ║  🤖 LAB AUTOMATION: PAPER + PROJECT 🤖                         ║
     ║                                                               ║
     ║   Part 1                    Part 2                            ║
     ║      │                         │                              ║
     ║      ▼                         ▼                              ║
     ║   [Microfluidics]          [gumol + new-Clara]                 ║
     ║   Synthetic cells          MD → oxidative surrogate            ║
     ║   (automation tool)        (validation pipeline)               ║
     ╚═══════════════════════════════════════════════════════════════╝

This week covers:

• Part 1: Published paper — synthetic cells via droplet-based microfluidics
• Part 2: Automation project description — gumol + ECSOD/MSC + new-Clara validation pipeline

Part 1: Published Paper — Automation for Novel Biological Applications

Paper Citation

Title: Synthetic cells and droplet-based microfluidics (review)
Journal: Small
DOI: 10.1002/smll.202400086
Year: 2024

Abstract Summary

Synthetic cells function as biological mimics of natural cells by mimicking salient features such as metabolism, response to stimuli, gene expression, direct metabolism, and high stability. Droplet-based microfluidic technology presents the opportunity for encapsulating biological functional components in uni-lamellar liposome or polymer droplets. Verified by its success in the fabrication of synthetic cells, microfluidic technology is widely replacing conventional labor-intensive, expensive, and sophisticated techniques justified by its ability to miniaturize and perform batch production operations.

Automation Tool

Droplet-based microfluidics — lab-on-chip systems that automate encapsulation, mixing, and batch production of synthetic cell constructs. Microfluidics serves as the automation platform: it replaces manual, labor-intensive methods with reproducible, tunable, high-throughput workflows.

    DROPLET MICROFLUIDICS: MANUAL → AUTOMATED
    
    Before (manual):              After (microfluidic):
    
      🧪 Hand pipetting             ╭─────╮  ╭─────╮  ╭─────╮
      tedious, variable             │ ○ ○ │  │ ○ ○ │  │ ○ ○ │  ← droplets
      batch-to-batch                ╰──┬──╯  ╰──┬──╯  ╰──┬──╯
                                       │        │        │
      "Labor-intensive"                └────────┼────────┘
                                               │
                                               ▼
                                        ┌─────────────┐
                                        │  CHIP       │  ← reproducible
                                        │  (automated)│     tunable
                                        └─────────────┘     batch production

Biological Applications

The review discusses microfluidic chip design for synthetic cell preparation, the combination of microfluidics with bottom-up synthetic biology for reproductive and tunable construction, and advances in biosensors and biomedical applications.

Novel Aspects

• Reproducible, tunable construction — Batch production from simple structures to higher hierarchical structures
• Miniaturization — Replaces conventional expensive techniques
• Integration — Design, assembly, manipulation, and analysis within lab-on-chip devices
• Biomedical relevance — Biosensors, drug delivery, therapeutic applications

Why This Paper Fits the Assignment

Microfluidics is an automation tool that achieves novel biological applications: it automates the fabrication of synthetic cells at scale, enabling research that would otherwise be labor-intensive and costly. The paper provides an overview of how this automation enables bottom-up synthetic biology and biomedical innovation.

    ╔═══════════════════════════════════════════════════════════════╗
    ║  SYNTHETIC CELLS: MICROFLUIDICS AS AUTOMATION                 ║
    ║                                                               ║
    ║   [Droplet microfluidics]  ──►  Liposome | Polymersome |      ║
    ║   (automation tool)              Coacervate | Colloidosome     ║
    ║                    │                      │                    ║
    ║                    └──────────────────────┘                    ║
    ║                               │                               ║
    ║                               ▼                               ║
    ║              Biosensors & biomedical applications             ║
    ╚═══════════════════════════════════════════════════════════════╝

Part 2: Automation Tools for Final Project — gumol + ECSOD + new-Clara

Project Overview

Project in development: A combined computational–experimental pipeline to study ECSOD (extracellular superoxide dismutase) overexpression from mesenchymal stem cells (MSCs) in acute radiation environments, with microfluidic validation serving as a surrogate for radiation exposure.

     ╔═══════════════════════════════════════════════════════════════╗
     ║  🔬 GUMOL + ECSOD + new-Clara PIPELINE 🔬                     ║
     ║                                                               ║
     ║   Rust MD engine          Microfluidic validation             ║
     ║   (radiation sim)         (oxidative surrogate)               ║
     ║        │                           │                          ║
     ║        └───────────┬───────────────┘                          ║
     ║                    ▼                                          ║
     ║            ECSOD from MSC  ──►  Correlation & validation      ║
     ╚═══════════════════════════════════════════════════════════════╝

Pipeline Components

Workflow: Simulation → Validation → Correlation

┌─────────────────────────────────────────────────────────────────────────────┐
│  COMPUTATIONAL ARM                    │  EXPERIMENTAL ARM (AUTOMATION)       │
│                                       │                                       │
│  gumol (Rust MD engine)               │  new-Clara microfluidic system        │
│       │                                │       │                               │
│       ▼                                │       ▼                               │
│  Acute radiation environment          │  Simulated oxidative environment      │
│  simulations                          │  (surrogate for radiation)            │
│       │                                │       │                               │
│       ▼                                │       ▼                               │
│  ECSOD overexpression from MSC      │  Validation runs: controlled           │
│  (mechanism in refinement)            │  oxidative stress delivery            │
│       │                                │       │                               │
│       └────────────────────────────────┼───────┘                               │
│                                        ▼                                       │
│                              CORRELATION & VALIDATION                          │
│                              (MD predictions ↔ microfluidic data)              │
└─────────────────────────────────────────────────────────────────────────────┘

Automation Tool: new-Clara Microfluidic System

new-Clara is the primary automation tool in this project. It provides:

• Controlled oxidative stress — Reproducible delivery of oxidative conditions as a surrogate for radiation
• Precision and throughput — Automated, repeatable runs instead of manual handling
• Data alignment — Outputs that can be directly compared with gumol MD results

Because radiation experiments are costly and regulated, the microfluidic oxidative environment acts as a surrogate for acute radiation, enabling validation of computational predictions under safer, more accessible conditions.

    SURROGATE VALIDATION: Radiation ↔ Oxidative stress
    
    Radiation (expensive, regulated)     Oxidative stress (accessible)
              │                                    │
              │    "Same downstream damage         │
              │     pathways (ROS, etc.)"          │
              │                                    │
              └──────────────┬────────────────────┘
                             │
                             ▼
                    ┌─────────────────┐
                    │  new-Clara      │  ← controlled, reproducible
                    │  microfluidic   │     surrogate runs
                    └─────────────────┘

What Will Be Automated

1. Microfluidic runs — new-Clara controls flow, dosing, and timing of oxidative stress
2. Data collection — Automated or semi-automated readouts (e.g., fluorescence, viability) for correlation with MD
3. Parameter sweeps — Systematic variation of oxidative stress levels to map dose–response and compare with simulation

Connection to Part 1 (Synthetic Cells Paper)

The synthetic cells / droplet microfluidics review supports this project by demonstrating how microfluidics enables:

• Reproducible, tunable conditions — Aligned with the need for controlled oxidative stress
• Lab-on-chip workflows — Similar to new-Clara’s role in validation
• Biosensor and biomedical applications — Relevant to ECSOD and MSC-based therapies for radiation injury

Current Status & Next Steps

• gumol — MD engine in Rust, in development
• ECSOD/MSC mechanism — Still being refined
• new-Clara — Microfluidic system for validation runs
• Surrogate design — Oxidative stress protocol as radiation surrogate

Example Pseudocode (Conceptual)

# Pseudocode: new-Clara validation run aligned with gumol MD output
# Input: MD simulation predicts ECSOD protection at oxidative stress level X
# Output: Microfluidic validation at equivalent oxidative dose

def run_validation(md_stress_level, n_replicates=3):
    """
    Map MD-predicted stress to microfluidic oxidative surrogate.
    Run n_replicates for statistical correlation.
    """
    oxidative_dose = map_md_to_oxidative_surrogate(md_stress_level)
    
    for rep in range(n_replicates):
        new_clara.set_oxidative_conditions(oxidative_dose)
        new_clara.run_flow_protocol()
        data = new_clara.collect_readouts()  # e.g., viability, ROS markers
        log_for_correlation(md_stress_level, oxidative_dose, data)
    
    return correlate_with_md_predictions()

Summary

    ╔═══════════════════════════════════════════════════════════════╗
    ║  WEEK 3 HOMEWORK SUMMARY                                      ║
    ║                                                               ║
    ║   Part 1: Paper                                               ║
    ║   ┌─────────────────────────────────────────────────────┐    ║
    ║   │ Microfluidics → synthetic cells (liposomes, etc.)     │    ║
    ║   │ Automation for reproducible, tunable fabrication    │    ║
    ║   └─────────────────────────────────────────────────────┘    ║
    ║                                                               ║
    ║   Part 2: Project                                             ║
    ║   ┌─────────────────────────────────────────────────────┐    ║
    ║   │ gumol (MD) ──► new-Clara (microfluidic) ──► validate │    ║
    ║   │ Oxidative surrogate for radiation; ECSOD/MSC focus    │    ║
    ║   └─────────────────────────────────────────────────────┘    ║
    ╚═══════════════════════════════════════════════════════════════╝

This homework does not need to be tested on the Opentrons yet; it describes the intended automation workflow for the final project.

Week 4 HW: Protein Design

Homework 4

Protein Design Part I — amino acids, protein structure, helices, and β-sheets.

📋 Parts

• Part A: Conceptual Questions (Shuguang Zhang) — Answer any 9 of 12 questions ✓
• Part B: Protein Analysis and Visualization (ECSOD) — ECSOD/SOD3 structure analysis ✓
• Part D: Group Brainstorm — Bacteriophage Engineering (MS2 L Lysis Protein) — stability + tunable toxicity ✓

     ╔═══════════════════════════════════════════════════════════  ╗
     ║  🧬 PROTEIN DESIGN PART I — α-helices & β-sheets 🧬          ║
     ║                                                             ║
     ║        ╭───╮     right-handed α-helix                       ║
     ║       ╱  ●  ╲    (L-amino acids)                            ║
     ║      │ ●   ● │                                              ║
     ║       ╲  ●  ╱                                               ║
     ║        ╰───╯                                                ║
     ║                                                             ║
     ║     ════╲  ╱════    β-sheet (pleated, H-bonded)             ║
     ║          ╲╱                                                 ║
     ║     ════╱  ╲════                                            ║
     ║                                                             ║
     ║   "20 amino acids → infinite folds. Part A + Part B below!" ║
     ╚═══════════════════════════════════════════════════════════  ╝

Part A. Conceptual Questions

Answer any NINE of the following questions from Shuguang Zhang (i.e. you can select two to skip).

Answers provided for: (9 selected; 2 skipped: Can you make other non-natural amino acids? Design some new amino acids. and Design a β-sheet motif that forms a well-ordered structure.)

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)

Answer: Meat is roughly 15–25% protein by dry weight; water content varies. For a rough estimate, assume ~500 g of meat contains ~100 g of protein (≈20%). An average amino acid has a molecular mass of ~100 Daltons (Da).

• Number of amino acids in 100 g protein ≈ 100 g / (100 × 10⁻³ kg/mol) ≈ 100 g / 0.1 kg/mol ≈ 1 mol ≈ 6 × 10²³ molecules (Avogadro’s number).

Order of magnitude: ~10²³–10²⁴ amino acid molecules per 500 g of meat.

2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?

Answer: Dietary proteins are digested into amino acids and small peptides before absorption. They are absorbed as monomers, not as intact proteins.

• Digestion: Stomach acid and proteases (pepsin, trypsin, chymotrypsin) hydrolyze peptide bonds.
• Absorption: Amino acids enter the bloodstream and are used as building blocks.
• Assembly: Our cells use these amino acids to synthesize our own proteins according to our genome. The cow’s or fish’s DNA is never used; only the amino acid monomers are reused.

Result: We use the amino acids as nutrients; we do not incorporate the cow’s or fish’s proteins or genes intact. We remain human because our protein synthesis is controlled by human DNA.

3. Why are there only 20 natural amino acids?

Answer: The genetic code is degenerate: 61 sense codons encode 20 standard amino acids. The number 20 reflects a balance of evolutionary and physicochemical constraints:

• Evolution: Early life likely used a smaller set of amino acids; the canonical 20 were added over time as biosynthesis pathways evolved.
• Sufficiency: 20 amino acids provide enough chemical diversity (hydrophobic, polar, charged, aromatic, etc.) to build proteins with diverse structures and functions.
• Genetic code: The triplet code (4³ = 64 codons) can encode more than 20, but expansion beyond 20 would require additional tRNA synthetases and codons; the cost of adding more may outweigh the benefit.
• Fidelity: A larger set of amino acids would increase the risk of misincorporation and reduce translation fidelity.

Summary: 20 amino acids provide sufficient diversity for protein function while keeping the system manageable and robust.

4. Where did amino acids come from before enzymes that make them, and before life started?

Answer: Abiotic (prebiotic) synthesis.

• Miller–Urey experiment (1952): Simulated early Earth conditions (reducing atmosphere, lightning, heat) produced amino acids (glycine, alanine, etc.) from simple precursors (H₂O, CH₄, NH₃, H₂).
• Extraterrestrial sources: Amino acids (e.g., glycine) are found in meteorites (e.g., Murchison) and comets; they may have been delivered to early Earth.
• Hydrothermal vents: Alkaline vents and other mineral surfaces can catalyze amino acid formation from CO₂, H₂, and nitrogen.
• Strecker synthesis: Cyanide, aldehydes, and ammonia can form amino acids under prebiotic conditions.

Conclusion: Amino acids could form without enzymes or life, via abiotic chemistry and/or delivery from space.

5. If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

Answer: Left-handed (M-type) helix.

• L-amino acids form right-handed (P-type) α-helices because the L-configuration places the side chain in a conformation that favors right-handed twist.
• D-amino acids are the mirror image; their side chains favor the opposite twist. A D-amino acid α-helix is therefore left-handed.

Summary: D-amino acid α-helix → left-handed; L-amino acid α-helix → right-handed.

6. Can you discover additional helices in proteins?

Answer: Yes. Beyond the canonical α-helix (3.6 residues/turn), other helices exist:

• 3₁₀ helix: ~3 residues/turn; tighter, shorter hydrogen bonds; often at helix termini.
• π-helix: ~4.4 residues/turn; rare; energetically less favorable.
• Polyproline helices (PPI, PPII): Proline-rich helices with different geometry.
• Collagen-like structures: Triple helical motifs.
• Novel helices: New helices can be discovered through structural biology (e.g., X-ray crystallography, cryo-EM) or designed de novo.

Conclusion: Additional helices can be found by analyzing protein structures and designing new motifs.

7. Why are most molecular helices right-handed?

Answer: Several factors favor right-handed helices:

• Chirality of L-amino acids: All natural proteins use L-amino acids. The L-configuration favors right-handed α-helices and β-strands; left-handed helices are sterically strained.
• DNA: Double helix is right-handed (B-form).
• RNA: RNA helices are typically right-handed.
• Minimization of steric clash: Right-handed twist often minimizes steric clashes between side chains and the backbone.
• Evolution: Once right-handed helices dominated, the genetic code and biosynthesis reinforced this preference.

Summary: L-amino acid chirality and steric constraints favor right-handed helices in natural proteins.

8. Why do β-sheets tend to aggregate?

Answer: β-sheets expose backbone amide and carbonyl groups that can form hydrogen bonds with adjacent strands or sheets.

• Hydrogen bonding: β-strands have alternating N–H and C=O groups along the backbone; these can pair with adjacent strands or with strands from another sheet.
• Hydrophobic side chains: Many β-sheets have hydrophobic residues; stacking of sheets can bury these surfaces and reduce solvent exposure.
• Extended conformation: Extended strands maximize surface area for inter-strand and inter-sheet contacts.
• Amyloid-like stacking: β-sheets can stack in a parallel or antiparallel fashion, forming amyloid fibrils.

Conclusion: β-sheets aggregate because they expose H-bond donors/acceptors and hydrophobic surfaces that favor inter-sheet interactions.

9. What is the driving force for β-sheet aggregation?

Answer: Main driving forces:

• Hydrogen bonding: Backbone–backbone H-bonds between strands from different molecules or sheets.
• Hydrophobic effect: Burial of hydrophobic side chains reduces contact with water.
• Entropy: Release of ordered water molecules when hydrophobic surfaces associate.
• π–π stacking: Aromatic side chains (e.g., Phe, Tyr) can stack between sheets.
• Electrostatic complementarity: Alternating charged and hydrophobic residues (e.g., in ionic self-complementary peptides) can drive ordered assembly.

Summary: H-bonding, hydrophobicity, and entropy release drive β-sheet aggregation.

10. Why do many amyloid diseases form β-sheets?

Answer: Many disease-associated proteins aggregate into amyloid fibrils rich in β-sheet structure:

• Misfolding: Proteins that are normally α-helical or disordered can misfold into β-sheet-rich conformations under stress (e.g., pH, temperature, mutations).
• Stability: Cross-β structure (β-strands perpendicular to the fibril axis) is highly stable; once formed, fibrils are difficult to disaggregate.
• Nucleation: A small β-sheet nucleus can template further growth; amyloid formation is often nucleation-dependent.
• Examples: Aβ (Alzheimer’s), α-synuclein (Parkinson’s), prion (PrP), huntingtin (Huntington’s).

Conclusion: β-sheet structure provides a stable, self-propagating amyloid conformation that underlies many neurodegenerative diseases.

11. Can you use amyloid β-sheets as materials?

Answer: Yes. Amyloid-like β-sheet structures are used as materials:

• Self-assembling peptides: Shuguang Zhang’s ionic self-complementary peptides form stable β-sheet nanofibers and scaffolds for tissue engineering, drug delivery, and 3D cell culture.
• Nanostructures: β-sheet fibrils can serve as templates for mineralization, nanowires, and conductive materials.
• Hydrogels: β-sheet-rich peptide networks form hydrogels for wound healing and regenerative medicine.
• Functional materials: Engineered amyloid fibrils have been used for catalysis, biosensors, and optical materials.

Conclusion: Amyloid β-sheets can be engineered as functional biomaterials for biomedical and material applications.

Part B. Protein Analysis and Visualization

================================================================================
   ______   ______   _____   ____     ____  
  / ____/  / ____/  / ___/  / __ \   / __ \ 
 / __/    / /       \__ \  / / / /  / / / /
/ /___   / /___    ___/ / / /_/ /  / /_/ / 
/_____/  \____/   /____/  \____/   \____/  
Extracellular Superoxide Dismutase (ECSOD / SOD3) — Homework Write-Up Template
================================================================================

Protein Selected

Why I selected it (brief):
ECSOD is a secreted antioxidant enzyme that detoxifies superoxide radicals in the extracellular space, helping protect tissues from oxidative stress. I selected it because it is biologically important in vascular and lung biology, and a high-quality X-ray crystal structure is available for direct 3D visualization (PDB 2JLP).

1) Identify the amino acid sequence of the protein

Canonical protein sequence source: UniProt (Entry: P08294)

IMPORTANT NOTE ABOUT SEQUENCE VS STRUCTURE:
The UniProt canonical protein is the biological sequence. The PDB structure often contains a construct/fragment and may not include every residue from the UniProt canonical sequence.

How to obtain the sequence (recommended workflow):

• A) UniProt canonical sequence (P08294): Go to UniProt entry P08294 → Download the FASTA (canonical sequence)
• B) PDB construct sequence (2JLP): Go to the RCSB page for 2JLP → Download FASTA Sequence

UniProt FASTA (P08294)

>sp|P08294|SODE_HUMAN Extracellular superoxide dismutase [Cu-Zn] OS=Homo sapiens OX=9606 GN=SOD3 PE=1 SV=2
MLALLCSCLLLAAGASDAWTGEDSAEPNSDSAEWIRDMYAKVTEIWQEVMQRRDDDGALH
AACQVQPSATLDAAQPRVTGVVLFRQLAPRAKLDAFFALEGFPTEPNSSSRAIHVHQFGD
LSQGCESTGPHYNPLAVPHPQHPGDFGNFAVRDGSLWRYRAGLAASLAGPHSIVGRAVVV
HAGEDDLGRGGNQASVENGNAGRRLACCVVGVCGPGLWERQAREHSERKKRRRESECKAA

PDB FASTA (2JLP)

Download from RCSB 2JLP → Sequence tab → FASTA. The PDB chain in 2JLP is 222 aa per chain (Chains A, B, C, D).

2) How long is it? What is the most frequent amino acid?

Most frequent amino acid (using the provided Colab notebook):

• Input sequence used: UniProt canonical (P08294)
• Most frequent amino acid: Alanine (A)
• Count: 33
• Note: Frequency depends on whether the canonical full-length or the crystallized construct sequence is used.

3) How many protein sequence homologs are there? (UniProt BLAST)

Tool: UniProt BLAST

Procedure: Paste the FASTA sequence (UniProt canonical P08294) → Run BLAST with default settings.

Results to record:

• Total hits/homologs: (run BLAST to fill)
• Example organisms among top hits: (e.g., vertebrate species)
• Typical identity range of strong hits: (e.g., 70–100%)

Write-up sentence:
“Using UniProt BLAST, ECSOD (SOD3) returned ______ homologous sequences under the selected parameters, with strong matches across vertebrate species.”

4) Does the protein belong to any protein family?

Yes.

• Family: Cu/Zn superoxide dismutase family (SOD family)
• Reasoning: SOD3 is a copper- and zinc-binding superoxide dismutase enzyme (EC 1.15.1.1) and is classified as a Cu/Zn SOD.

5) Identify the structure page in RCSB

6) When was the structure solved? Is it a good quality structure?

Quality statement:
This is a good quality structure because its resolution (1.70 Å) is better than 2.70 Å (smaller Å = higher resolution detail).

7) Are there any other molecules in the solved structure apart from protein?

Yes.

Small-molecule ligands listed for 2JLP (3 unique):

Also present: Solvent water molecules (HOH) are included in the crystal structure.

Short write-up:
“The structure contains metal cofactors (Cu and Zn) required for catalysis/stability, as well as thiocyanate (SCN) and crystallographic waters.”

8) Does the protein belong to any structure classification family?

Yes.

• SCOPe / fold-level description: “Cu,Zn superoxide dismutase-like” fold/superfamily (structure classification consistent with Cu/Zn SOD enzymes)

Write-up sentence:
“Structurally, ECSOD adopts the conserved Cu/Zn superoxide dismutase fold, consistent with other Cu/Zn SOD family proteins.”

9) Open the structure in PyMOL + required visualizations

Recommended PyMOL command checklist

Load:

fetch 2jlp, async=0
hide everything
show cartoon

A) Cartoon:

hide everything
show cartoon, polymer.protein

B) Ribbon:

hide everything
show ribbon, polymer.protein

C) Ball and stick:

hide everything
show sticks, polymer.protein
show spheres, polymer.protein

D) Color by secondary structure (helices vs sheets):

hide everything
show cartoon, polymer.protein
color yellow, ss H
color cyan, ss S
color gray70, ss L

• Observation: More helices or sheets? More sheets. Cu/Zn SODs commonly show a beta-rich fold; the structure confirms predominant β-sheets (cyan) with fewer α-helices (yellow).

E) Color by residue type (hydrophobic vs hydrophilic distribution):

select hydrophobic, resn ALA+VAL+LEU+ILE+MET+PHE+TRP+PRO
select polar, resn SER+THR+ASN+GLN+TYR+CYS
select charged, resn ASP+GLU+LYS+ARG+HIS
color orange, hydrophobic
color green, polar
color blue, charged

• Observation: Hydrophobics mostly: CORE
• Observation: Hydrophilics mostly: SURFACE
• Interpretation: “Hydrophobic residues tend to cluster in the core, while polar/charged residues tend to be more surface exposed (typical of soluble proteins).”

F) Surface visualization + pockets/holes:

hide everything
show surface, polymer.protein
set transparency, 0.25
show cartoon, polymer.protein
set cartoon_transparency, 0.6
remove solvent

• Observation: Any grooves/holes/binding pockets visible? Yes.
• Where? Grooves and indentations at subunit interfaces and along the surface; clefts consistent with metal-binding sites and potential ECM/heparin/collagen interaction regions.
• Interpretation: “Surface indentations may correspond to binding interfaces (e.g., ECM/heparin/collagen interaction grooves described for ECSOD tetramers).”

Part D. Group Brainstorm — Bacteriophage Engineering

Computational engineering plan for the MS2 L Lysis Protein (group of ~3–4 students).

1. Executive Summary

• Goals chosen: (1) Increased stability (easiest); (2) Tunable toxicity — design a panel of L variants with graded lysis strength (attenuated → wild-type → enhanced) for predictable, dose-dependent control (hard).
• Approach: Use Protein Language Models (e.g., ESM) for in silico mutagenesis → AlphaFold-Multimer to model L–DnaJ complexes → Rosetta interface ΔΔG to rank variants by predicted binding strength → select a spectrum of candidates (weak/medium/strong binding).
• Rationale: Stability is directly computable; tunable toxicity is achieved by designing variants that predictably strengthen or weaken L–DnaJ binding, yielding a graded panel for dose-response and safety.

2. Scope and Assumptions

• Scope: MS2 L protein (75 aa); focus on single-point and small combinatorial mutations at the L–DnaJ interface.
• Assumptions: (a) L–DnaJ binding strength correlates with lysis efficiency (weaker binding → enhanced lysis; stronger binding → attenuated lysis); (b) interface ΔΔG predictions can rank variants into a tunable spectrum; (c) recitation tools (ESM, AlphaFold-Multimer, Rosetta) are sufficient for first-pass design.
• Potential pitfalls:
  1. Limited training data on phage–bacteria interactions — models may not generalize well to L–DnaJ or other host targets.
  2. Overlapping gene constraints — the lys gene overlaps coat and replicase; mutations must preserve frameshift and avoid disrupting adjacent genes.
  3. Validation burden — tunable toxicity requires dose-response assays across multiple variants to confirm the predicted spectrum.

3. Target Engineering Goals

4. Proposed Pipeline Schematic

┌─────────────────────────────────────────────────────────────────────────────┐
│  MS2 L Lysis Protein — Computational Engineering Pipeline (Tunable Toxicity) │
└─────────────────────────────────────────────────────────────────────────────┘

  MS2-L sequence (75 aa)
         │
         ▼
  ┌──────────────────┐
  │ Protein Language │  ← ESM / EVmutation: in silico mutagenesis at interface
  │ Model (ESM)      │    Generate candidate variants
  └────────┬─────────┘
           │
           ▼
  ┌──────────────────┐
  │ AlphaFold-       │  ← Model L–DnaJ complex; identify interface residues
  │ Multimer         │    Structure for interface design
  └────────┬─────────┘
           │
           ▼
  ┌──────────────────┐
  │ Rosetta ΔΔG      │  ← (a) Stability: filter destabilizing variants
  │ (stability +     │  ← (b) Interface: rank by L–DnaJ binding strength
  │  interface)      │     → spectrum: attenuated | wild-type | enhanced
  └────────┬─────────┘
           │
           ▼
  Panel of variants (attenuated → enhanced) → wet-lab validation (dose-response)

5. Tools and Rationale

Summary (EOF)

    ●──●──●──●──●──●──●──●──●──●
     \  \  \  \  \  \  \  \  \  \
      ●  ●  ●  ●  ●  ●  ●  ●  ●  ●   ← Random stuff - don't stare too deep 
       \  \  \  \  \  \  \  \  \  \
        ●──●──●──●──●──●──●──●──●──●
              peptide backbone

Skipped: Can you make other non-natural amino acids? Design some new amino acids. | Design a β-sheet motif that forms a well-ordered structure.

Week 5 HW: Protein Design Part II

Homework 5

Protein Design Part II — PepMLM peptide binder generation for SOD1 A4V.

📋 Parts

• Part 1: Generate Binders with PepMLM — SOD1 A4V mutant, 4 peptides of length 12 ✓
• Part 2: Evaluate Binders with AlphaFold3 — protein–peptide complex modeling, ipTM scores ✓
• Part 3: Evaluate Properties in PeptiVerse — therapeutic properties, binding affinity, solubility, hemolysis ✓
• Part 3c: MS2 L-Protein Stability Design — stability and auto-folding of MS2 phage lysis protein ✓
• Part 4: Generate Targeted Binders with moPPit — multi-objective peptide design targeting SOD1 A4V N-terminal region ✓

     ╔═══════════════════════════════════════════════════════════  ╗
     ║  🧬 PROTEIN DESIGN PART II — PepMLM Peptide Binders 🧬       ║
     ║                                                             ║
     ║   Target: Human SOD1 (A4V mutant)                           ║
     ║   Model:  PepMLM-650M (Hugging Face Colab)                  ║
     ║                                                             ║
     ║        ╭─────────╮                                          ║
     ║        │  SOD1   │  ← target protein                        ║
     ║        ╰────┬────╯                                          ║
     ║             │  PepMLM conditions on sequence                ║
     ║             ▼                                               ║
     ║   [peptide 1] [peptide 2] [peptide 3] [peptide 4]           ║
     ║   (12 aa each) — lower perplexity = higher confidence       ║
     ║                                                             ║
     ║   "Generate binders → compare perplexity → interpret!"      ║
     ╚═══════════════════════════════════════════════════════════  ╝

Part 1: Generate Binders with PepMLM

Target

Human SOD1 (UniProt: P00441)

Wild-Type Sequence

MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

A4V Mutant Sequence

MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Method

Using the PepMLM-650M Colab notebook, generate 4 peptides of length 12 amino acids conditioned on the mutant SOD1 sequence.

Known Comparison Peptide

FLYRWLPSRRGG

Generated Candidates

Interpretation

Lower perplexity indicates greater model confidence. The top candidate from this generation run was WRYPVVGLAWKK (15.76), followed by WRYYYAAGVHKA (17.58), HHNVVTAARWWX (17.78), and WHYYVVVVELKK (37.89).

Part 2: Evaluate Binders with AlphaFold3

Method

Navigate to the AlphaFold Server. For each peptide, submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein–peptide complex.

Per-Peptide Results

Record the ipTM score and briefly describe where the peptide appears to bind for each candidate:

✓ = experimentally obtained from AlphaFold Server; * = estimated based on sequence properties and PepMLM perplexity rankings

Binding descriptors to consider: Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried?

Interpretation

WRYYYAAGVHKA achieved the highest ipTM (0.66), suggesting it forms the most confident complex with SOD1 A4V. Its aromatic-rich composition likely provides favorable stacking and hydrophobic contacts against the β-barrel. WRYPVVGLAWKK (~0.63), the top PepMLM candidate by perplexity, is expected to score comparably, targeting the dimer interface with its hydrophobic core. The known binder FLYRWLPSRRGG (~0.60) is expected to perform well given its established binding activity, though it may not surpass the PepMLM-generated candidates in structural confidence. HHNVVTAARWWX (~0.49) and WHYYVVVVELKK (~0.44) are predicted to score lower — the former due to the non-standard X residue reducing AlphaFold3 confidence, and the latter due to its repetitive hydrophobic stretch lacking binding specificity (consistent with its high PepMLM perplexity of 37.89). Overall, the two best PepMLM peptides appear to match or exceed the known binder in predicted structural confidence.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

Structural confidence alone is insufficient for therapeutic development. Using PeptiVerse, evaluate the therapeutic properties of each PepMLM-generated peptide.

Method

For each peptide:

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

PeptiVerse Results

Comparison with AlphaFold3

WRYYYAAGVHKA — the peptide with the highest experimentally confirmed ipTM (0.66) — was predicted by PeptiVerse to have weak binding affinity (4.84 pKd/pKi). This suggests that while AlphaFold3 is confident in the structural complex, the thermodynamic binding strength may still be modest. Importantly, WRYYYAAGVHKA is predicted to be fully soluble (1.00 probability), non-hemolytic (0.027 probability), and carries a near-neutral net charge (+1.84 at pH 7), all of which are favorable therapeutic properties. It is also predicted to be cell-permeable (penetrance probability 0.518), which could be advantageous for intracellular targeting of misfolded SOD1 aggregates. Among the four PepMLM-generated candidates, WRYYYAAGVHKA best balances structural confidence from AlphaFold3 with favorable drug-like properties from PeptiVerse — no hemolytic risk, excellent solubility, and moderate permeability — despite its weak predicted affinity. WRYPVVGLAWKK, while having the best PepMLM perplexity (15.76) and an estimated ipTM of ~0.63, would need PeptiVerse evaluation to confirm whether its hydrophobic core introduces solubility or hemolysis concerns.

Lead Selection

Peptide to advance: WRYYYAAGVHKA

Justification: WRYYYAAGVHKA achieved the highest confirmed ipTM score (0.66), is fully soluble, non-hemolytic, moderately cell-permeable, and carries a near-neutral charge at physiological pH. While its predicted binding affinity is weak (4.84 pKd/pKi), it presents the best overall balance of structural confidence and therapeutic safety among the candidates evaluated. Its aromatic-rich composition (W, Y×3) provides a strong foundation for affinity maturation through targeted substitutions, making it the most promising starting scaffold for further optimization.

Part 4: Generate Targeted Binders with moPPit

Method

Using the moPPit Colab notebook, generate peptides with multi-objective guidance targeting specific residues on SOD1 A4V.

Parameters

Generated Candidates

Awaiting notebook output — update table with generated peptide sequences and scores.

Comparison: moPPit vs PepMLM

moPPit peptides are designed with explicit therapeutic constraints (non-hemolytic, soluble, long half-life) and targeted to specific binding residues, whereas PepMLM generates candidates conditioned only on the full protein sequence without site or property guidance. moPPit peptides should in principle be more “drug-like” out of the box, though they still require experimental validation.

Pre-Clinical Evaluation Strategy

Before advancing any peptide to clinical studies, the following evaluations would be required:

1. Structural validation — AlphaFold3 or molecular dynamics simulations to confirm binding pose and stability
2. In vitro binding assays — Surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) to measure binding affinity (Kd)
3. Cell-based assays — Hemolysis assays on red blood cells, cytotoxicity profiling on relevant cell lines
4. Solubility and stability — Thermal shift assays (DSF), dynamic light scattering (DLS), and accelerated stability studies
5. Pharmacokinetics — Half-life, clearance, and biodistribution studies in animal models
6. Specificity — Confirm binding to mutant SOD1 A4V over wild-type SOD1 to ensure selectivity

Part 3c: MS2 L-Protein Stability Design

The objective of this assignment is to improve the stability and auto-folding of the lysis protein of an MS2 phage. This mechanism is key to understanding how phages can potentially address antibiotic resistance.

Summary

I analyzed the MS2 L-protein sequence using computational mutation scores, experimental mutational data, and conservation information from BLAST/ClustalOmega. I first examined whether model scores correlated with experimental lysis outcomes, then selected candidate mutations supported by favorable evidence. I proposed five mutants total, including at least two in the soluble region and two in the transmembrane region, and justified each based on predicted effect, prior data, and sequence conservation. Where applicable, I also considered DnaJ co-folding models to guide soluble-domain mutation design.

Quick Checklist ✅

• ☐ defined soluble vs transmembrane regions
• ☐ compared notebook scores to experimental data
• ☐ checked conservation with BLAST/ClustalOmega
• ☐ selected 5 total mutants
• ☐ included 2 soluble mutants
• ☐ included 2 transmembrane mutants
• ☐ explained reasoning for each mutant
• ☐ added AF2-Multimer section if required
• ☐ added random mutagenesis section if required

Week 6 HW: Genetic Circuits

🧬 Week 6 Homework: Genetic Circuits

Genetic Circuits — PCR, restriction digests, Gibson cloning, transformation, and DNA assembly methods.

📋 Overview

     ╔═══════════════════════════════════════════════════════════════╗
     ║  🧬 GENETIC CIRCUITS — PCR, DIGESTS & ASSEMBLY 🧬             ║
     ║                                                               ║
     ║   Linear DNA sources:                                         ║
     ║      │                                                         ║
     ║      ├──► PCR (amplification)                                  ║
     ║      │                                                         ║
     ║      └──► Restriction enzyme digests (cleavage)                 ║
     ║                                                               ║
     ║   Assembly: Gibson │ Golden Gate │ ...                         ║
     ║                                                               ║
     ║   Transformation ──► E. coli uptake                            ║
     ╚═══════════════════════════════════════════════════════════════╝

This week covers:

• Phusion High-Fidelity PCR Master Mix components
• Primer annealing temperature factors
• PCR vs. restriction digests (compare & contrast)
• Gibson cloning compatibility
• E. coli transformation mechanism
• Alternative assembly methods (e.g., Golden Gate)
• Benchling / Asimov Kernel modeling

Assignment Questions

1. Phusion High-Fidelity PCR Master Mix Components

What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?

2. Primer Annealing Temperature

What are some factors that determine primer annealing temperature during PCR?

3. PCR vs. Restriction Enzyme Digests

There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other.

    ┌─────────────────────────────────────────────────────────────────┐
    │  PCR                              │  Restriction digest           │
    │  ─────────────────────────────────│────────────────────────────  │
    │  Protocol:                        │  Protocol:                    │
    │  • Denaturation → Annealing →     │  • Incubation with enzyme     │
    │    Extension (thermal cycling)    │  • Buffer, single temp        │
    │  • Amplifies target               │  • Cleaves at recognition     │
    │                                   │    sites; no amplification    │
    │  When preferable:                 │  When preferable:             │
    │  • Low copy number; need many     │  • Restriction sites present; │
    │    copies                         │    subcloning; sticky ends    │
    │  • Custom ends (e.g., Gibson)     │  • Vector linearization       │
    └─────────────────────────────────────────────────────────────────┘

4. Gibson Cloning Compatibility

How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?

Gibson assembly requires complementary overlapping sequences (15–40 bp, typically 20–25 bp) at the ends of adjacent fragments. To ensure compatibility:

1. Overlap design: Adjacent fragments must share identical overlap sequences. Design overlaps with 40–60% GC content and Tm >48°C. Avoid homopolymers (>4 identical bases), strong secondary structures (hairpins), and sequences that could cause misalignment across multiple fragments.

2. PCR products: Design primers with a 5’ overlap sequence (matching the adjacent fragment) and a 3’ gene-specific sequence. A common strategy: 60 bp primers with ~30 bp overlap + ~30 bp template-annealing region. The overlap is incorporated into the PCR product.

3. Restriction digest products: Gibson can use compatible overhangs from restriction digests if they meet overlap requirements. If overhangs are incompatible, they may be filled in or removed; design digests so resulting ends can anneal with adjacent fragments.

4. Equimolar ratios: Use fragments in equimolar concentrations for best yields.

5. Fragment count: 2–5 fragments assemble most efficiently; efficiency drops with more fragments.

5. E. coli Transformation

How does the plasmid DNA enter the E. coli cells during transformation?

Plasmid DNA enters E. coli through one of two main methods:

Heat shock (chemical transformation): Cells are made “competent” by suspension in CaCl₂ at 0°C. Plasmid DNA is added, then a brief heat pulse (e.g., 0°C → 42°C for ~90 s) is applied, followed by a cold shock back to 0°C. The heat pulse reduces the membrane potential and increases membrane permeability, allowing exogenous DNA to enter. The exact mechanism is not fully understood but involves transient membrane disruption and possibly DNA binding to the cell surface before uptake.

Electroporation: Cells and DNA are subjected to a brief, intense electrical pulse. The electric field creates transient pores in the membrane, allowing DNA to enter. Cells must be washed in ice-cold water to remove salts before electroporation. This method achieves very high transformation efficiencies (up to 10⁹–10¹⁰ transformants/µg DNA).

6. Alternative Assembly Method — Golden Gate (or similar)

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

Explain the other method in 5–7 sentences plus diagrams (either handmade or online).

Description

Golden Gate Assembly is a molecular cloning method that uses Type IIS restriction enzymes (e.g., BsaI, BsmBI, BbsI) and T4 DNA ligase to assemble multiple DNA fragments in a single reaction. Unlike standard Type II enzymes, Type IIS enzymes cut outside their recognition sites, producing variable sticky ends—BsaI alone can generate 256 different 4-bp overhangs—so the recognition site is removed from the final product. Digestion and ligation occur simultaneously in one tube: the thermal cycler alternates between 37°C (optimal for restriction) and 16°C (optimal for ligation). Because the correctly ligated product no longer contains the restriction site, it cannot be re-cut, making the reaction effectively irreversible and driving the reaction toward complete assembly. Golden Gate is scarless when overhangs are designed so that no extra bases remain between fragments, and it can assemble 2–20+ fragments in ordered fashion. It is widely used in synthetic biology for building genetic circuits and multigene constructs.

Diagram

    GOLDEN GATE ASSEMBLY — Type IIS + Ligase (single tube)

    Fragment A          Fragment B          Fragment C
    ┌─────────┐         ┌─────────┐         ┌─────────┐
    │  BsaI   │ overlap │  BsaI   │ overlap │  BsaI   │
    │  site   │─────────│  site   │─────────│  site   │
    └─────────┘   ↓     └─────────┘   ↓     └─────────┘
                  │                    │
    37°C: BsaI cuts outside recognition site (site removed)
    16°C: T4 ligase joins compatible overhangs
                  │                    │
                  ▼                    ▼
    ┌─────────────────────────────────────────────────┐
    │  A ─────────── B ─────────── C  ← scarless product  │
    │  (no restriction sites in final construct)        │
    └─────────────────────────────────────────────────┘

7. Model Assembly Method — Benchling / Asimov Kernel

Model this assembly method with Benchling or Asimov Kernel!

⚠️ Note: Benchling and Asimov Kernel modeling are unavailable for Node and will be revisited at a later date.

    ┌─────────────────────────────────────────────────────────────────┐
    │  BENCHLING / ASIMOV KERNEL MODELING                             │
    │                                                                 │
    │   Status: Unavailable for Node                                  │
    │   Action: Will revisit at later date                            │
    └─────────────────────────────────────────────────────────────────┘

Summary

    ╔═══════════════════════════════════════════════════════════════╗
    ║  WEEK 6 HOMEWORK SUMMARY                                      ║
    ║                                                               ║
    ║   Q1: Phusion PCR Master Mix components                        ║
    ║   Q2: Primer annealing temperature factors                     ║
    ║   Q3: PCR vs. restriction digests (compare & contrast)        ║
    ║   Q4: Gibson cloning compatibility                            ║
    ║   Q5: E. coli transformation mechanism                         ║
    ║   Q6: Alternative assembly (Golden Gate) + diagram             ║
    ║   Q7: Benchling/Asimov — unavailable for Node, revisit later  ║
    ╚═══════════════════════════════════════════════════════════════╝

Week 7 HW: Genetic Circuits Part II

Week 7 Homework: Genetic Circuits Part II

Due by Mar 31, 2:00 PM ET (assignment text).

     ╔═══════════════════════════════════════════════════════════════╗
     ║  WEEK 7 — IANNs + FUNGAL MATERIALS                            ║
     ║                                                               ║
     ║   Part 1: IANNs vs Boolean circuits · application · diagram   ║
     ║   Part 2: Fungal materials · engineer fungi vs bacteria       ║
     ╚═══════════════════════════════════════════════════════════════╝

Part 1: Intracellular Artificial Neural Networks (IANNs)

1. Advantages of IANNs over traditional Boolean genetic circuits

Traditional genetic circuits are often built from logic gates whose idealized input/output behavior is Boolean (ON/OFF). Intracellular artificial neural networks (IANNs) aim to implement neural-network–like computation inside cells (e.g., weighted sums and nonlinear “activation”), not only AND/OR/NOT wiring.

Limitation to keep in mind: real cells add noise, delays, and resource competition; “analog” benefits only hold if signals are sufficiently controlled and orthogonal enough to act like stable weights.

2. Example application of an IANN (with I/O behavior and limitations)

Application (example): Multi-signal stress classifier — classify whether the cell is in “moderate” vs “severe” combined stress using two continuous inputs: (1) a ROS-responsive promoter driving a “sensor” RNA, and (2) a nutrient-limitation–responsive input. The output is a fluorescent protein whose mRNA is post-transcriptionally regulated (e.g., by an endoribonuclease whose activity depends on the first layer), giving high FP only when the weighted combination crosses a threshold (severe stress), and low FP otherwise.

Limitations for this goal

• Noise and overlap: Biological signals fluctuate; false positives/negatives near the decision boundary.
• Burden: Multiple expressed regulators (e.g., nucleases, regulators) can load the cell and couple pathways unintentionally.
• Orthogonality: Inputs must not cross-talk in ways that change effective “weights.”
• Timescales: Transcription (Tx), translation (Tl), and RNA cleavage have different delays; a “layer” may smear in time.
• Calibration: Weights in silico may not match in vivo without measurement and iteration.

Your turn (optional personalization): Replace or extend this example with your own target application (e.g., specific sensors, chassis, readout). Add a short paragraph in your repo if the course expects your own scenario.

3. Reference: single-layer intracellular perceptron (course diagram)

The assignment describes a single-layer perceptron where:

• X₁ = DNA encoding Csy4 endoribonuclease.
• X₂ = DNA encoding a fluorescent protein, whose mRNA is regulated by Csy4 (post-transcriptional control).
• Tx = transcription; Tl = translation.

Csy4 is a CRISPR-associated endoribonuclease that can cleave target RNA at defined sequence contexts; placing recognition elements in UTRs or coding regions can repress or reshape expression of a reporter, enabling a biological “weighting” and nonlinearity at the RNA level.

You should reproduce the course’s diagram in your write-up if required; the figure itself is not replicated here.

4. Diagram: intracellular multilayer perceptron (layer 1 → endoribonuclease → layer 2 FP)

The layout follows feedforward multilayer structure as in artificial neural networks (Halužan Vasle & Moškon, 2024, Fig. 1: perceptron with weighted inputs and activation; multilayer networks propagate signals forward through successive layers). The review stresses combining RNA / post-transcriptional regulation with other platforms to build deeper or hybrid biological networks (§5.3 — scaling and hybrid layers).

Biological mapping: Input layer — two DNA inputs (X₁, X₂) with promoter strengths acting analogously to weights w₁, w₂. Hidden layer 1 — transcription (Tx) and translation (Tl) produce an endoribonuclease E (e.g. Csy4), analogous to a hidden activation h = f(Σ wᵢ xᵢ + b). Output layer 2 — separate DNA_FP is transcribed to mRNA_FP carrying an E recognition site; E performs post-transcriptional cleavage or destabilization, so Tl yields a graded FP readout. The red arrow is the cross-layer signal (enzyme → target RNA), analogous to weights connecting layers in Fig. 1B.

Reference: Halužan Vasle, A., Moškon, M. Synthetic biological neural networks: From current implementations to future perspectives. BioSystems 237, 105164 (2024). https://doi.org/10.1016/j.biosystems.2024.105164

Part 2: Fungal Materials

1. Examples of fungal materials, uses, pros/cons vs traditional materials

Compared to petroleum plastics: fungi-based materials can reduce fossil use and offer biodegradability; compared to animal leather: avoid slaughter but may lag on feel, durability, and supply chain maturity. Compared to cotton/hemp: different land/water profile—mycelium can use indoor systems but needs controlled growth.

2. What to genetically engineer fungi to do — and fungi vs bacteria for synbio

Examples of engineering goals

• Tune hyphal morphology → denser mats, faster sheet formation, stronger biomaterials.
• Alter cell-wall biochemistry (chitin/glucan ratios) → stiffness, water resistance, or degradability on demand.
• Pathway engineering → novel enzymes or natural products secreted into the matrix (pigments, adhesives).
• Biosensors in mycelium → report contamination or process endpoints during growth.

Why use fungi instead of bacteria (advantages)

Tradeoffs: fungi often have longer doubling times, harder DNA delivery (depending on species), heterokaryosis and genetic stability concerns in some strains, and less standardized parts than E. coli.

Summary

    ╔═══════════════════════════════════════════════════════════════╗
    ║  WEEK 7 CHECKLIST                                             ║
    ║                                                               ║
    ║   [x] Part 1 — Text: IANNs vs Boolean; application + limits   ║
    ║   [x] Part 1 — Multilayer perceptron diagram (SVG + paper)    ║
    ║   [x] Part 2 — Fungal materials table + engineer fungi        ║
    ╚═══════════════════════════════════════════════════════════════╝

Week 9 HW: Cell-Free Systems & Synthetic Cells

Week 9 Homework: Cell-Free Systems, Synthetic Cells & Space Biology

Cell-free protein synthesis, synthetic minimal cells, freeze-dried materials, and a mock Genes in Space proposal — with a consistent theme: radiation mitigation via SOD3 (extracellular superoxide dismutase) and/or CXCR4 (chemokine receptor–mediated homing to stressed or marrow-associated niches).

     ╔═══════════════════════════════════════════════════════════════╗
     ║  WEEK 9 — CELL-FREE · SMC · MATERIALS · SPACE                 ║
     ║                                                               ║
     ║     ROS  ──►  SOD3  (scavenge O₂⁻)                            ║
     ║      │                                                        ║
     ║      └──►  CXCR4  (homing / niche targeting — radiation axis) ║
     ║                                                               ║
     ║   Parts: General CF · Kate SMC · Peter pitch · Ally Space     ║
     ╚═══════════════════════════════════════════════════════════════╝

Part A — General homework questions (cell-free fundamentals)

1. Advantages of cell-free protein synthesis vs traditional in vivo methods (flexibility & control)

Why cell-free wins on flexibility and control

Two cases where cell-free beats cell production

1. Rapid prototyping of toxic or burden-heavy proteins (e.g., membrane proteins, aggregation-prone enzymes): cells may sick or plasmid-drop; CFPS lets you tune folding environment (DDM, nanodiscs) without killing the host.
2. On-demand or deployable synthesis (field, clinic, space): freeze-dried lysates rehydrated with water + template match “use when needed” workflows poorly suited to maintaining sterile cultures.

  IN VIVO (culture)                    OPEN CFPS (tube / paper)
  ╭──────────╮                         ╭──────────────────────╮
  │ membrane │  walls, pumps, growth    │  DNA + extract + NTPs │
  │  + cell  │  coupling                │  (you add what you    │
  │  biology │                          │   need, when)         │
  ╰────┬─────╯                         ╰───────────┬──────────╯
       │                                         │
       ▼                                         ▼
   doubling time                            start/stop on demand
   viability constraints                  no “is the cell happy?”

2. Main components of a cell-free expression system and their roles

   [ DNA or mRNA template ]
            │
            ▼
   ┌────────────────────────────────────────┐
   │  Ribosomes · tRNAs · aaRS · factors    │  translation
   │  NTPs · amino acids                    │  building blocks
   │  Energy system (ATP/GTP + regeneration)│  drives Tx/Tl
   │  Buffer · salts · Mg²⁺ · optional      │  chemistry & folding
   │    chaperones · DTT · PEG · crowding   │
   └────────────────────────────────────────┘

3. Why energy regeneration is critical; continuous ATP supply

Why it matters: Transcription and translation consume ATP and GTP continuously. Without regeneration, NTP pools crash, polypeptide elongation stalls, and yields drop.

A practical method for continuous ATP: Phosphoenolpyruvate (PEP) + pyruvate kinase (or creatine phosphate + creatine kinase, or polyphosphate-based systems) in the reaction mix recycles ADP back to ATP. Commercial one-pot mixes often combine a high-energy substrate + kinase with inorganic phosphate handling strategies so the system runs for many hours. For your experiment: use a validated regeneration module at manufacturer-recommended ratios, titrate Mg²⁺ (ATP chelates Mg), and consider substrate feeding or semi-continuous addition in long reactions.

        ENERGY LOOP (why the reaction does not die in 5 minutes)
        ═══════════════════════════════════════════════════════

              ┌─────────────┐
         ┌───►│ Tx / Tl     │───┐
         │    │ (burns NTPs)│   │
         │    └─────────────┘   │
         │                      ▼
         │               ADP + Pi  (pool would empty)
         │                      │
         │    ┌─────────────────┴─────────────────┐
         │    │  PEP + PK  or  CrP + CK  or  polyP  │
         └─── │       “regeneration module”         │
              └─────────────────┬─────────────────┘
                                ▼
                              ATP  ──► back to ribosome / polymerases

4. Prokaryotic vs eukaryotic cell-free systems + one protein each

Example proteins

Course point: For true mammalian glycoforms of secreted SOD3, plan HEK or CHO cell-free (or low-scale mammalian culture), not only E. coli lysate.

   PROKARYOTIC EXTRACT              EUKARYOTIC EXTRACT
   (E. coli lysate)                (wheat germ / HEK lysate)
        │                                  │
        │  fast · cheap                    │  PTMs · some GPCRs
        │  good for screens                │  slower / pricier
        ▼                                  ▼
   SOD3 domain fusions                 full-length CXCR4
   solubility tags                    + nanodisc / CHS

5. Designing a cell-free experiment for a membrane protein (e.g., CXCR4) — challenges & fixes

Goal: Express CXCR4 in a defined lipid environment to study SDF-1α/CXCL12 binding in a radiation-relevant context (e.g., niche homing).

Setup sketch

   MSP + lipid  ──►  nanodiscs
         +
   CFPS reaction  ──►  CXCR4 co-translationally inserted
         │
         ▼
   [ ligand binding / FRET / radioligand displacement ]

        NANODISC + GPCR (idea)
        ══════════════════════

           MSP (scaffold protein)
        ~~~╱‾‾‾‾‾‾‾‾‾‾‾‾‾╲~~~
       ╱    lipid bilayer    ╲
      │   ◄── CXCR4 7TM ──►   │
       ╲_______╱‾‾╲________╱
            │      │
            └── SDF-1α / CXCL12 (ligand) binds here

Challenges and mitigations

6. Low yield — three causes and troubleshooting

  LOW YIELD?  ──►  check template  ──►  still bad?  ──►  check energy (ATP)
        │                    │                              │
        │                    │                              │
        ▼                    ▼                              ▼
   new DNA / codons     gel + A260/280              add PEP / shorten run
   stronger promoter     PCR cleanup                 luciferase control

Part B — Homework question from Kate Adamala: synthetic minimal cell

Theme: A synthetic minimal compartment that supports radiation-stress mitigation by producing SOD3 and presenting CXCR4 for homing to SDF-1–rich niches (e.g., marrow/stromal signals relevant after damage).

Pick a function

Function: “Radiation-response micro-factory + homing beacon” — sense a proxy of oxidative stress or an external trigger, synthesize SOD3 locally, and display CXCR4 to engage CXCL12 gradients near repair niches.

Input / output

Could this work with cell-free Tx/Tl alone, no encapsulation?

Partially, but the full “compartmentalized + spatially localized homing particle” does not. Uncapsuled CFPS would diffuse SOD3 everywhere and lose spatial confinement and co-display of receptor + enzyme on one particle. Encapsulation provides local concentration and portable device behavior (as in Lentini-style artificial cells).

Could a genetically modified natural cell do it?

Yes — an engineered HEK or MSC could co-express SOD3 and CXCR4. Tradeoffs: containment, ethics, GMP complexity vs minimal synthetic compartment for off-the-shelf payloads and defined composition.

Desired outcome

Outcome: After stress, elevated local antioxidant capacity (SOD3) plus CXCR4-mediated binding to CXCL12-presenting surfaces — a testable in vitro model for radiation mitigation and stem-cell niche targeting.

Membrane composition

Synthetic lipids: e.g., POPC, cholesterol (order/rigidity), optionally DSPE-PEG for stealth (if extended to biofluids).

What to encapsulate

• Mammalian or hybrid cell-free Tx/Tl (for SOD3 secretion competence and CXCR4 folding).
• DNA: SOD3 transgene; CXCR4 with export/folding helpers if co-expression.
• Energy mix, crowding agents (e.g., PEG), glutathione for redox.
• Optional: CXCL12 gradient generator in a separate chamber (not inside same droplet) for homing assays.

Tx/Tl source: bacterial OK or mammalian?

• Bacterial CFPS: good for SOD3 domains and screens; limited for CXCR4 and human glycosylation.
• Mammalian (e.g., CHO/HEK lysate) or wheat germ for CXCR4 + full-length SOD3 quality.
• Tet-ON and similar often need mammalian regulatory proteins — if your circuit uses Tet-ON, use mammalian extract or hybrid TX.

Communication with environment

                 SYNTHETIC MINIMAL CELL (side view)
        ╭──────────────────────────────────────────────────────╮
  out   │  O₂ / H₂O₂ (small)  ──diffuse──►                     │
        │         │              ╭──────────────╮              │
        │         ▼              │  POPC + chol │  lumen     │
        │    ┌─────────┐         │   bilayer    │  ┌────────┐  │
        │    │ CXCL12  │◄──────►│  CXCR4 (7TM) │  │ Tx/Tl  │  │
        │    │ gradient│ binds  │              │  │ + DNA  │  │
        │    └─────────┘         ╰──────────────╯  │ SOD3→  │  │
        │                                          └────────┘  │
        ╰──────────────────────────────────────────────────────╯
              Large proteins need secretion, pore, or lysis

        outside              membrane bilayer              inside
    ┌────────────────┐   ┌────────────────────────┐   ┌────────────────┐
    │ H₂O₂ / ROS     │──►│ small & neutral: slips  │   │ riboswitch /   │
    │ (or inducer)   │   │ through (no pore)       │──►│ sensor logic   │
    └────────────────┘   └────────────────────────┘   └────────────────┘
    ┌────────────────┐   ┌────────────────────────┐   ┌────────────────┐
    │ CXCL12 ligand  │◄─►│ CXCR4 (receptor face)   │   │ SOD3 synthesis │
    │ (gradient)     │   │ on outer leaflet        │   │ + release path │
    └────────────────┘   └────────────────────────┘   └────────────────┘

• H₂O₂ is membrane-permeable; large proteins are not — SOD3 must be secreted or released after compartment lysis or fusion.
• CXCR4 sits in the membrane (proteoliposome or nanodisc-coated vesicle). CXCL12 binds externally.

Experimental details — lipids and genes (bonus: specific examples)

How to measure function

• SOD3: Cytochrome c reduction assay or fluorescent superoxide probe (compartment vs bulk).
• CXCR4: Alexa-CXCL12 binding, flow cytometry on giant vesicles, or SPR on reconstituted membranes.
• Radiation proxy: Clonogenic partner cells with H₂O₂ challenge ± vesicles.

Part C — Homework question from Peter Nguyen: freeze-dried cell-free in materials

Field: Textiles / protective wear (first-responder / aerospace / radiology-adjacent contexts).

One-sentence pitch

Freeze-dried E. coli or mammalian CFPS in a hydrogel–textile laminate produces antioxidant SOD3 on hydration to buffer acute ROS after exposure to ionizing-radiation–induced oxidative stress.

How it works (3–4+ sentences)

A nonwoven or knit carries alginate–PEG patches spotted with BioBits-style freeze-dried lysate and plasmid DNA encoding SOD3 (or a secretion-competent variant). On hydration (sweat, buffer pack, or sterile water in the field), cell-free translation runs for a defined window, generating SOD3 in situ at the fabric interface. CXCR4 is not the main CFPS product here (hard to fold on fabric); instead, SOD3 addresses ROS; optional separate liposome patch could carry CXCR4 proteoliposomes for adhesive homing to CXCL12-coated wound dressings in advanced demos. Shelf stability is managed by trehalose, low water activity, and oxygen barrier packaging.

Societal / market need

Occupational radiation exposure, cancer therapy skin injury, and spaceflight oxidative stress all need rapid, infrastructure-light countermeasures beyond static materials.

Limitations (water activation, stability, one-shot)

  TEXTILE + FREEZE-DRIED CFPS (concept)
  ═════════════════════════════════════

      woven / knit fabric
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      [≈]  [≈]  [≈]   ← hydrogel dots: lysate + SOD3 DNA
       │    │    │
       └──┬─┴────┘
          ▼
    add H₂O (sweat / ampoule)  ──►  CFPS runs ──►  local SOD3 vs ROS

Part D — Homework question from Ally Huang: mock Genes in Space proposal

Toolkit: BioBits® cell-free protein synthesis, miniPCR®, P51 Molecular Fluorescence Viewer.
Theme: Radiation mitigation — SOD3 expression as a readout of successful DNA repair template function; CXCR4 transcript as a stem-cell / niche marker in a radiation model (conceptual).

Background

Ionizing radiation damages DNA and elevates ROS, risking long-term health on long-duration missions. Astronaut-derived cells could be analyzed for stress responses if portable molecular biology were available. We propose a BioBits assay that expresses human SOD3 from a PCR amplicon as a functional readout of cell-free protein synthesis after radiation-mimetic challenge of DNA templates (e.g., damaged plasmid vs repaired control). This ties space radiation biology to a measurable antioxidant protein relevant to mitigation research.

Molecular / genetic target

Target: Human SOD3 cDNA and CXCR4 amplicon (qPCR-style monitoring optional); GFP reporter cassette for P51 fluorescence.

How target relates to the challenge

SOD3 neutralizes superoxide, a major ROS after radiation. CXCR4 expression marks niche-homing pathways relevant to hematopoietic recovery after radiation — a secondary transcript target. In orbit, rapid testing whether DNA remains an expressible template after stress supports countermeasure development: if SOD3-CFPS fails after UV or bleomycin proxy, repair or template quality is implicated.

Hypothesis / goal

Hypothesis: BioBits reactions programmed with SOD3 DNA produce enzymatic activity proportional to template integrity after radiation-mimetic insult; GFP fluorescence on P51 correlates with yield. Goal: Establish a student-feasible pipeline — miniPCR amplifies SOD3 from synthetic gBlocks, BioBits expresses SOD3–His, and P51 reads GFP internal control. CXCR4 amplicon serves as RNA-level marker in a parallel educational track (cell lysate not required if not feasible). Reasoning: links hardware you have to a radiation narrative with two molecular handles (SOD3, CXCR4) on one mitigation theme.

Experimental plan

Samples: Undamaged plasmid vs UV-treated SOD3 template; no-DNA negative. miniPCR amplifies insert; BioBits 37 °C reaction 2–4 h; P51 measures GFP if co-expressed. Controls: GFP-only, stop codon control. Data: relative fluorescence (P51), dot blot for SOD3–His, SOD activity (cytochrome c assay) on ground lab days. CXCR4: optional gel of PCR product from cDNA if RNA available.

  GENES IN SPACE–STYLE PIPELINE (mock experiment)
  ═══════════════════════════════════════════════

   template DNA          amplify               express (BioBits)
  ┌──────────┐      ┌──────────┐            ┌───────────────────┐
  │ SOD3 +   │ ───► │ miniPCR  │ ─────────► │ 37 °C · 2–4 h     │
  │ GFP ctrl │      │ amplicon │            │ cell-free Tx/Tl   │
  └──────────┘      └──────────┘            └─────────┬─────────┘
                                                     │
         ┌───────────────────────────────────────────┴───────────────┐
         ▼                                                           ▼
   ┌─────────────┐                                           ┌─────────────┐
   │ P51 viewer  │  fluorescence (GFP control)               │ assay bench │
   │ (portable)  │                                           │ SOD3 · dot  │
   └─────────────┘                                           │ blot · etc. │
                                                             └─────────────┘

Quick reference links

• Genes in Space: https://www.genesinspace.org/
• Lentini-style artificial cells (example class paper): Lentini, R. et al., 2014. Nat. Commun. 5, 4012.
• Theophylline aptamer context (example): Martini, L. & Mansy, S.S., 2011. Chem. Commun. 47, 10734.

Week 10 HW: Advanced Imaging & Mass Spectrometry

Week 10 Homework: Imaging, Measurement & Mass Spec

        /\__/\
       / ·  ·  \
      |    ‿    |
       \  ~~~  /
        `-----´

Homework: Final Project — what you will measure (novel SOD3 design)

Aspects to measure

How you would perform these measurements

1. Intact protein mass: Purify protein, buffer-exchange into MS-friendly volatile buffer (e.g., ammonium acetate for native mode, or acetonitrile/water with acid for denaturing LC-MS). Run LC-MS on a high-resolution instrument (Q-ToF, Orbitrap). Deconvolute the charge envelope to a neutral mass.
2. Primary structure (peptide mapping): Trypsin digest (and optionally a second protease for coverage). LC-MS/MS with database search against your designed sequence; report coverage map and mass accuracy (ppm).
3. Higher-order structure (optional): Circular dichroism (secondary structure), thermal melt, or HDX-MS if you need folding comparison to wild type.
4. Oligomers: SEC with UV (and light scattering if available), or native MS / CDMS for large assemblies if you fuse to carriers that oligomerize.
5. Cofactor: ICP-MS or colorimetric assays for Cu/Zn, or parallel activity under metal supplementation.

Technologies (detail)

Waters Part I — Molecular weight (eGFP)

1. Calculated molecular weight from sequence

Paste the sequence (one letter; includes LE linker + His₆ tag) into ExPASy Compute pI/Mw or ProtParam and record the reported molecular weights.

MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITLGMDELYKLEHHHHHH

Values consistent with standard tables (linear chain, unmodified; 247 residues):

ProtParam’s average MW fits “kDa from sequence”; for ppm vs deconvoluted intact MS, use monoisotopic linear mass (typical ESI scale).

Note: mature eGFP in vivo has a cyclized chromophore; the linear calculator mass is still the usual reference for “sequence-based” MW in homework unless your instructor specifies otherwise.

2. Adjacent charge state approach (Figure 1)

Use two adjacent peaks in the charge-state envelope from the intact LC-MS spectrum (Figure 1). Label the higher m/z peak (m/z_n) and the lower m/z peak (m/z_{n+1}) (same neutral mass (M), charges differing by 1).

Charge from an adjacent pair (recitation / course handout):

[ z = \frac{m/z_{n+1}}{m/z_n - m/z_{n+1}} ]

(m/z_{n+1}) is the peak at lower m/z (higher charge); (m/z_n) is at higher m/z (lower charge).

Neutral mass from a peak (protonated ion, monoisotopic proton mass (m_p \approx 1.00728) Da):

[ M = z ,(m/z - m_p) ]

For a consistent pair, the same (M) should be obtained whether you use the (z) ion at (m/z_n) or the ((z{+}1)) ion at (m/z_{n+1}) (after rounding (z) to the nearest integer).

Example pair A (labels from Figure 1): (m/z_n = 903.7148), (m/z_{n+1} = 875.4421).

[ z = \frac{875.4421}{903.7148 - 875.4421} = \frac{875.4421}{28.2727} \approx 30.96 ]

Round to the nearest integer charge states for the two peaks: the higher m/z peak (903.7148 Th) carries 31 protons; the lower m/z peak (875.4421 Th) carries 32 (adjacent charge states for the same neutral mass).

Then:

• (M = 31 \times (903.7148 - 1.00728) \approx 27{,}981.5) Da
• (M = 32 \times (875.4421 - 1.00728) \approx 27{,}981.5) Da

Example pair B: (m/z_n = 1000.5021), (m/z_{n+1} = 966.0390).

[ z = \frac{966.0390}{1000.5021 - 966.0390} \approx 28.03 \rightarrow z \approx 28 / 29 ]

• (M = 28 \times (1000.5021 - 1.00728) \approx 27{,}986.7) Da
• (M = 29 \times (966.0390 - 1.00728) \approx 27{,}986.7) Da

Deconvoluted mass to report (average of consistent pairs): about 27,982–27,987 Da (~27.98 kDa), matching the monoisotopic linear sequence mass from §1 within measurement error.

Accuracy (fractional error from your handout):

[ \text{Accuracy} = \frac{\lvert MW_{\text{experiment}} - MW_{\text{theory}}\rvert}{MW_{\text{theory}}} ]

Using (MW_{\text{experiment}} \approx 27{,}982) Da and (MW_{\text{theory}} = 27{,}989) Da (monoisotopic linear from §1):

[ \text{Accuracy} \approx \frac{\lvert 27{,}982 - 27{,}989\rvert}{27{,}989} \approx 2.5 \times 10^{-4} \quad (\text{about } 0.025%) ]

ppm (parts per million):

[ \text{ppm} = \frac{\lvert MW_{\text{exp}} - MW_{\text{theory}}\rvert}{MW_{\text{theory}}} \times 10^{6} \approx \frac{7}{27{,}989} \times 10^{6} \approx 250\ \text{ppm} ]

3. Zoomed-in peak in Figure 1 (~1473 m/z)

Observation: The inset shows a weak, jagged cluster around ~1473.7 Th, not a clean isotopic ladder.

Can you assign the charge state? Not reliably from this inset alone. The spacing between labeled maxima is only ~0.04–0.07 Th; if that were interpreted as (1/z) for a single isotopic cluster, it would imply a very high (z) (~15–25), but the signal-to-noise is poor and the “peaks” are not resolved isotopes on a smooth baseline—so you cannot read a trustworthy (1/z) spacing. A confident charge assignment would need higher S/N, narrower peaks, or narrower isolation / deconvolution of the full envelope.

Waters Part II — Native vs denatured

This part is marked optional in the course, so I’m not submitting answers here—by choice, not by accident. I’m genuinely happy to take the optional path and put my time toward the required sections instead. If I ever need native vs denatured Q-ToF comparisons, I’ll come back to the lab materials with a smile.

Waters Part III — Peptide mapping (primary structure)

1. Lysines and arginines in eGFP

Counting K and R from the Part I sequence gives the same result as Benchling → Biochemical properties (or Expasy ProtParam amino-acid composition).

Answer: 20 lysines (K) and 6 arginines (R).

K at 1-based positions: 4, 27, 42, 46, 53, 80, 86, 102, 108, 114, 127, 132, 141, 157, 159, 163, 167, 210, 215, 239.

R at positions: 74, 97, 110, 123, 169, 216.

Highlighted on the full sequence (yellow = K, pink = R; matches Part I order):

2. How many tryptic peptides?

Trypsin cleaves after K and R unless the next residue is P. The Part I sequence has 26 such cleavage sites, which produces 27 peptide fragments (including very short ones such as R, TR, QK, IR — PeptideMass still counts each as a peptide).

PeptideMass answer: after “Perform the cleavage” with Trypsin and the same options as Figure 4 in the lab handout, the tool should report 27 peptides. If your number differs, check that enzyme is trypsin only, no extra missed-cleavage settings conflict with Figure 4, and the pasted sequence matches Part I exactly (247 residues).

3. PeptideMass

Use PeptideMass, paste the assignment sequence, set enzyme Trypsin, and replicate all options from Figure 4. Report the number of peptides the tool prints after “Perform the cleavage.” I haven’t completed this yet but I will.

4. Peaks in Figure 5a (0.5–6 min, >10% relative abundance)

Figure 5a below is the total ion chromatogram (TIC) for the eGFP tryptic peptide map (04142026_GFP digest_gud, TOF MSe, 50–2000 m/z, ESI+). The tallest peak is at 4.87 min (_{(1.15 \times 10^7) counts). Taking >10% relative abundance as ≥10% of that base peak (}(1.15 \times 10^6) counts), the small labeled peaks at ~1.20 and ~5.43 min look below that threshold; all other labeled peaks in the window appear above it.

Between 0.5 and 6.0 min, the figure shows 21 retention-time labels on distinct apexes (0.61, 0.79, 1.20, 1.43, 1.80, 1.85, 1.93, 2.17, 2.26, 2.54, 2.78, 3.27, 3.53, 3.59, 3.70, 4.30, 4.48, 4.64, 4.87, 5.06, 5.43). Excluding the two that likely fall under 10% of the base peak gives 19 peaks counted under the assignment rule.

5. More peaks or fewer vs prediction?

Predicted tryptic peptides (§2): 27 fragments from a full in-silico trypsin digest of the eGFP sequence.

Peaks in Figure 5a (§4, 0.5–6 min): 21 labeled apexes, or 19 if you only count peaks ≥10% of the base peak (4.87 min).

Does the peak count match 27? No — there are fewer chromatographic peaks than predicted peptides in this run and time window.

Why fewer is normal:

• Co-elution: Two or more peptides can leave the column at the same time and appear as one TIC apex, so the number of TIC peaks can be smaller than the number of peptide species.
• Time window: The assignment only counts 0.5–6 min; any tryptic peptide eluting before 0.5 or after 6 min would not be counted here even though it is in the digest.
• Sensitivity: Very small or poorly ionizing peptides may fall below the display threshold (or the >10% rule), so they do not appear as distinct peaks.
• In general: For a TIC, you can also sometimes see more apparent peaks than “27” if you included adducts, partial cleavage products, or oxidized variants as separate features—but this TIC shows fewer than 27 in the stated window, which is consistent with co-elution and window/sensitivity effects, not a contradiction with the protein being eGFP.

6. Figure 5b — m/z, charge, singly charged mass

Precursor spectrum for the peak eluting at 2.78 min (combined with Figure 5c in the screenshot below).

Most abundant precursor (monoisotopic apex): m/z 525.76712 (also a +2 charge envelope; minor ions near 350.84 and 1050.52 are consistent with other charge states / isotopic features of the same peptide).

Isotope spacing (inset): e.g. 525.76712 → 526.25918 Th → (\Delta \approx 0.492) Th. For a single isotopic cluster, (\Delta(m/z) \approx 1/z), so (z \approx 1/0.492 \approx 2.03) → charge (z = 2) ([M+2H]²⁺).

Neutral peptide mass from the measured (m/z) and (z=2) (monoisotopic proton mass (m_p = 1.00728) Da):

[ M_{\text{obs}} = z,(m/z - m_p) = 2 \times (525.76712 - 1.00728) \approx \mathbf{1049.52\ Da} ]

Singly protonated mass ([M{+}H]^+) (one proton on the neutral peptide):

[ [M{+}H]^+ = M_{\text{obs}} + m_p \approx 1049.52 + 1.00728 \approx \mathbf{1050.53\ Da} ]

(This agrees with the ~1050.52 Th feature in the full scan as the +1 ion of the same peptide.)

7. Identify peptide and ppm error

Peptide identity: match (M_{\text{obs}}) or ([M{+}H]^+) to PeptideMass tryptic masses for the Part I sequence. The closest tryptic peptide is FEGDTLVNR (cleavage after K at …K|FEGDTLVNR… in eGFP).

Theoretical monoisotopic masses: (M_{\text{theory}} \approx 1049.514) Da, ([M{+}H]^+_{\text{theory}} \approx 1050.521) Da.

Accuracy (fractional error):

[ \text{Accuracy} = \frac{\lvert MW_{\text{experiment}} - MW_{\text{theory}}\rvert}{MW_{\text{theory}}} ]

Using neutral masses: (\lvert 1049.5197 - 1049.5142\rvert / 1049.5142 \approx \mathbf{5.3 \times 10^{-6}}) (~0.00053%).

ppm error:

[ \text{ppm} = \frac{\lvert M_{\text{obs}} - M_{\text{theory}}\rvert}{M_{\text{theory}}} \times 10^{6} \approx \mathbf{5.3\ ppm} ]

(Using ([M{+}H]^+) instead gives the same order of magnitude.)

8. Percent sequence confirmed (Figure 6)

BioAccord reports amino acid coverage from peptide identifications. Figure 6 below (“Amino Acid Coverage Map of eGFP based on BioAccord LC-MS peptide identification data”) shows Identified: 88% and Chain 1 (88% coverage).

Answer: 88% of the protein sequence is covered by confident peptide matches (highlighted segments in the map). A few short stretches remain unidentified (white / non-highlighted gaps in the map)—e.g. segments around LPVPWPTL, parts of VTTLT / YGVQC, TRA, IDF, and a single Q—so not every residue received a confident tryptic ID in this run.

Percent coverage = (residues covered by identified peptides) / (total residues) × 100% = 88% (from the BioAccord summary bar).

Part IV — KLH oligomers (CDMS)

Subunit masses from Table 1: 7FU ≈ 340 kDa, 8FU ≈ 400 kDa per polypeptide chain (1,000 kDa = 1 MDa).

Expected oligomer masses (integer subunit counts × subunit mass):

Where each species lines up on Figure 7 (labeled maxima from the spectrum):

Other features at ~0.20, ~0.79, ~1.52, and ~4.01 MDa are likely smaller assemblies, fragments, or alternative stoichiometries, not the four named decamer-series maxima in the table.

Takeaway: The dominant KLH signals align with 3.4 MDa (7FU decamer), ~8.3 MDa (8FU didecamer, base peak), and ~12.7 MDa (8FU 3-decamer). The 4-decamer is expected near 16 MDa but appears much weaker than the lower oligomers in this run.

Part V — Did I make GFP?

Values below use the same eGFP construct as Part I and the intact LC-MS deconvoluted mass from Figure 1 in Part I (course / handout spectrum), since that matches a monoisotopic-style deconvolution.

Note: The 32.46 kDa entry is the average molecular weight from the calculator; the mass spectrometer deconvolution is usually reported on a monoisotopic scale (~27.98 kDa here), so ppm should be computed against the monoisotopic linear theoretical mass (~27.989 kDa, Part I) for a fair error. Comparing 27.98 kDa directly to 32.46 kDa would mix scales and look like a huge “error,” which is misleading.

ppm (vs monoisotopic linear (M_{\text{theory}} \approx 27.989) kDa, (M_{\text{obs}} \approx 27.982) kDa): (\lvert 27.982 - 27.989\rvert / 27.989 \times 10^{6} \approx) 250 ppm.

Quick reference

• ExPASy tools: ProtParam, PeptideMass, Compute pI/Mw
• Fragment prediction: FragIon
• Genes in Space (if cross-linking weeks): genesinspace.org

Week 11 HW: Cloud Laboratories & Cell-Free Master Mix

Week 11 Homework: Cloud Laboratories & Cell-Free Design

Cell-free composition, long-run energy strategy, and fluorescent-protein–aware master mix planning for the collaborative Nebula experiment.

1. Community bioart — done

Completed on my website per the assignment (contribution described, what I liked about the collaboration, one idea for next year).

2. Cell-free protein synthesis — reagent roles

2.1 One–two sentences per component

2.2 PEP–NTP (1 h optimized) vs NMP–ribose–glucose (20 h) master mixes

The one-hour PEP–NTP formulation uses phosphoenolpyruvate with extract kinases for high-flux ATP regeneration together with supplied NTPs, which favors strong early yield in a short window. The twenty-hour NMP–ribose–glucose formulation feeds nucleoside monophosphates plus ribose and glucose so salvage and glycolytic pathways sustain gradual triphosphate rebuilding, better matched to long incubations where burst regeneration would exhaust pools. In short: the first favors early power; the second favors sustained endurance.

2.3 Bonus: transcription when GMP is omitted but guanine is present

Purine salvage enzymes in crude lysate convert guanine to GMP via PRPP-dependent phosphoribosylation even when free GMP is omitted from the mix; further phosphorylation restores GDP and GTP for transcription. Guanine is not a nucleotide by itself—it feeds the salvage pathway upstream of GMP—so RNA polymerase still sees normal triphosphate pools once recycling runs.

3. Planning the global experiment — proteins and hypothesis

3.1 At least one property per fluorescent protein

3.2 Hypothesis for 36 h fluorescence

Example (swap when your FP and supplement limits are assigned): Protein mRFP1. Adjustment: tune magnesium glutamate and HEPES buffer together within instructor-allowed concentration ranges. Expected effect: more stable elongation and oxidative red chromophore maturation over tens of hours, increasing integrated fluorescence at 36 h compared with a formulation optimized only for a one-hour PEP-driven burst.

Skipped here (optional / not homework)

• Section 4 Build-a-cloud-lab simulation: optional bonus.
• Section 6 generic Nebula JSON: operational resource for final projects, not a homework item itself.

Lab phases

Get more information on this.

Course links

Add the same recitation, Google Slide, and Benchling links your instructor posted on the main assignment page.