Overview
This year, the Notre Dame team primarily developed a comprehensive computational framework for rational protein engineering of GLP‑1 agonists, combining evolutionary bioinformatics with systematic experimental optimization strategies. Our work provides future iGEM teams with validated tools, methodologies, and insights for peptide hormone engineering and therapeutic protein design.
Open‑Source Computational Pipeline for GLP‑1 Engineering
We developed and released a complete bioinformatics pipeline for analyzing GLP‑1 homologs and identifying mutation‑tolerant positions for rational protein design. This pipeline integrates multiple state‑of‑the‑art tools to enable efficient and systematic protein engineering.
Pipeline Components
- Homolog Discovery: Automated BLAST and HMMER searches against UniProt databases to identify remote GLP‑1 homologs
- Multiple Sequence Alignment: Integration of SALIGN, MUSCLE, and Clustal Omega for robust alignment results
- Evolutionary Conservation Analysis: Shannon entropy calculations to quantify position‑specific conservation
- Structural Mapping: Python‑based visualization tools to map sequence conservation onto protein structures
- Software Availability: All scripts and analysis pipelines are freely available at gitlab.igem.org/2025/software-tools/notredame under the Creative Commons license, with complete documentation and installation instructions.
This pipeline enables future teams to:
- Rapidly identify evolutionarily conserved and variable positions in any protein of interest
- Make data‑driven decisions about which amino acids can be safely modified
- Reduce computational burden by focusing mutagenesis efforts on promising candidate positions
Shannon Entropy‑Based Rational Design Framework
We established a quantitative framework for identifying mutation‑tolerant positions in therapeutic peptides using Shannon entropy as a selection criterion. This approach enables systematic protein engineering while minimizing the risk of disrupting protein structure and function.
Design Principles
- High Entropy (> 2.0): Primary candidate positions; high variability indicates evolutionary tolerance to change and weaker purifying selection — ideal targets for engineering specificity and affinity.
- Medium Entropy (1.0–2.0): Proceed with caution; moderate conservation suggests some functional constraint. May tolerate conservative substitutions and require additional structural analysis.
- Low Entropy (< 1.0): Avoid modification; highly conserved positions are likely function‑critical and essential for folding, processing, or receptor engagement.
Theoretical Foundation: Evolutionarily conserved positions are integral to protein structure and function, while variable positions represent sites where natural selection has tolerated mutations. Focusing engineering efforts on high‑entropy positions reduces the combinatorial search space while preserving the likelihood of functional variants.
Application to GLP‑1: Using this framework, we identified candidate positions in the GLP‑1 sequence for engineering enhanced receptor specificity—transforming an intractable combinatorial problem into a manageable, rational design challenge.
Systematic Transformation Protocol Optimization Framework
Through extensive troubleshooting and systematic optimization, we developed a decision‑tree methodology for diagnosing and resolving transformation failures. This framework provides future teams with a structured approach to protocol optimization.
Three‑Factor Diagnostic Framework
- Plasmid Quality Assessment
- Verify plasmid optimization for the target organism
- Confirm DNA concentration and purity
- Check presence of fluorescent markers for screening
- Validate antibiotic resistance gene functionality
- Protocol Parameter Optimization
- Heat Shock Protocol: precise 30 s heat‑shock; strict ice incubation; ≥1 h recovery at 37°C in SOC; timely handling to avoid 2× yield losses per 10 min delay
- Electroporation: voltage optimization by species, competency series, media selection (SOC vs. LB), reagent quality
- Selection and Plating Optimization
- Antibiotics: Ampicillin for standard selection; Chloramphenicol for stronger pressure; plate‑level concentration optimization
Key Insight: While our specific experiments did not yield a fully optimized protocol—likely due to plasmid characteristics or equipment limitations—the framework enables rapid diagnosis and provides a roadmap for iterative optimization.
Impact for Future iGEM Teams
For Computational Teams
- Ready‑to‑use pipeline for evolutionary analysis of any protein
- Validated framework for rational protein design
- Reduced computational costs via informed site selection
For Wet Lab Teams
- Systematic troubleshooting methodology for transformation protocols
- Literature‑validated parameter ranges for heat shock and electroporation
- Decision tree for diagnosing transformation failures
For Therapeutic Protein Projects
- Demonstrated application of MSA and Shannon entropy to peptide hormone engineering
- Framework for balancing conservation and innovation in protein design
- Strategy for engineering specificity while maintaining function