EvoPro

Genetic algorithm-based protein binder optimization using AlphaFold2 and ProteinMPNN

Docs

Input

Target Protein

Mutable residues (optional)

Enable ProteinMPNN

400 credits

Output

Configure input settings on the left, then click "Submit job"

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

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What is EvoPro?