ScorpioLea/AiCE

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1144 stars · 186 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AiCE is a Python tool that predicts high-fitness protein mutations by sampling sequences from protein inverse folding models such as ProteinMPNN, LigandMPNN, ESM-IF1, and SaProt. It integrates structural and evolutionary constraints to nominate mutations that optimize protein function.

Use cases

• predict high-fitness mutations in a protein
• nominate mutations for directed evolution experiments
• optimize protein function using inverse folding models
• build LD and SCA coupling matrices from sampled sequences
• predict multi-mutation combinations for a monomeric protein structure

When to choose

• you have a protein structure (PDB) and want AI-nominated beneficial mutations
• you already use ProteinMPNN, LigandMPNN, ESM-IF1, or SaProt and want mutation prioritization
• you need structural and evolutionary constraints combined for protein engineering

When to avoid

• you need mutation prediction for protein complexes without modifying the provided scripts
• you lack a protein 3D structure for your target
• you need a general-purpose protein language model rather than mutation nomination

Facets

library · maturity active

machine-learning deep-learning data-science bioinformatics machine-learning artificial-intelligence python protein-engineering inverse-folding mutation-prediction computational-biology proteinmpnn directed-evolution jupyter-notebook linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ScorpioLea/AiCE")