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What is EvoPro?
EvoPro is a genetic algorithm-based pipeline for designing protein binders through in silico evolution. Developed by the Kuhlman Lab at the University of North Carolina, it combines iterative structure prediction with AlphaFold2 and sequence design with ProteinMPNN to evolve protein sequences that bind tightly to a target protein.
The approach differs from traditional computational design methods by allowing backbone plasticity during optimization. As sequences evolve across generations, their predicted structures can undergo conformational changes favorable for binding—something difficult to encode in physics-based design methods like Rosetta.
In published work, EvoPro generated autoinhibitory domains for a PD-L1 antagonist, with four designs achieving sub-150 nM binding affinity and the best reaching 0.9 nM without any experimental optimization.
How does EvoPro work?
EvoPro runs a genetic algorithm that maintains a population of candidate binder sequences and evolves them through repeated cycles:
- Scoring: Each sequence is evaluated by predicting its structure bound to the target using AlphaFold-Multimer
- Selection: Sequences are ranked by fitness; the bottom half is discarded
- Diversification: New sequences fill the population through mutation, crossover, or ProteinMPNN redesign
- Iteration: The cycle repeats for the specified number of generations
Fitness function
The fitness score combines three components from AlphaFold2 predictions:
| Component | What it measures |
|---|---|
| Placement confidence | Interface quality based on sidechain contacts weighted by PAE (predicted aligned error) |
| Fold confidence | Binder stability from average pLDDT across the designed protein |
| Conformational stability | RMSD between bound and unbound structures to minimize binding-induced changes |
Lower scores indicate better designs. The conformational stability term encourages rigid binders with fast association kinetics.
Sequence diversification
New sequences are generated through two strategies:
- Random mutagenesis + crossover: Introduces point mutations (~12.5% of residues) and recombines sequences from surviving parents
- ProteinMPNN optimization: Applied periodically (every ~10 generations), redesigns sequences using AlphaFold-predicted backbones as templates
How to use EvoPro online
ProteinIQ provides GPU-accelerated EvoPro runs without local installation, making binder design accessible through a browser interface.
Inputs
| Input | Description |
|---|---|
Target Protein | PDB file or RCSB PDB ID for the protein to design binders against |
Starting Scaffold | Optional starting structure or sequence. If omitted, EvoPro generates scaffolds automatically |
Evolution parameters
| Setting | Range | Default | Description |
|---|---|---|---|
Population size | 20–500 | 100 | Candidates per generation. Larger populations explore more diversity but increase runtime |
Number of generations | 5–200 | 50 | Evolutionary cycles. More generations improve optimization at the cost of time |
Mutation rate | 0.05–0.5 | 0.15 | Per-residue mutation probability. Higher rates increase diversity but may slow convergence |
Design settings
| Setting | Description |
|---|---|
Mutable residues | Restrict which positions can mutate. Use A* for all residues in chain A, or specify positions like A10,A15,A20. Leave empty to allow all positions |
Enable ProteinMPNN | Toggle ProteinMPNN sequence design during evolution (recommended) |
Quality thresholds
| Setting | Range | Default | Description |
|---|---|---|---|
pLDDT threshold | 50–95 | 70 | Minimum structure confidence. Higher values filter to more confident predictions |
PAE threshold | 5–30 | 15 | Maximum interface error. Lower values require higher confidence in the binding interface |
Results
EvoPro returns a ranked list of designed binders with:
| Column | Description |
|---|---|
Rank | Position in the ranked output (1 = best) |
Binding Score | Composite fitness score (lower = better) |
pLDDT | Average structure confidence (0–100) |
Sequence | Designed amino acid sequence |
Download | PDB file of the predicted complex |
The 3D viewer displays selected designs bound to the target protein.
Interpreting binding scores
| Score range | Interpretation |
|---|---|
| < -50 | Excellent candidate, high confidence interface |
| -50 to -30 | Good candidate, worth experimental validation |
| -30 to -10 | Moderate, may require optimization |
| > -10 | Weak prediction, likely needs redesign |
Scores depend heavily on target protein and starting scaffolds. Compare designs relative to each other rather than using absolute thresholds.
Limitations
- Runtime: Full evolutionary trajectories take 30–60 minutes depending on population size and generations
- Target size: Very large target proteins increase AlphaFold prediction time per generation
- No explicit binding energy: Fitness scores correlate with but don't directly predict experimental binding affinity
- Single binding mode: The algorithm optimizes for one binding interface; alternative binding sites aren't explored