Alterlab chai

Skill AlterLab-IEU/AlterLab-Academic-Skills/skills/bioinformatics/alterlab-chai

239 evaluated academic Claude/agent skills across 17 research domains (bioinformatics, data science, clinical, social-science methods, Turkish academia & more). Executable eval per skill, deterministic citation verifier, research→write→review→publish pipeline, and a skill-finder front door. Claude Code, Cursor, Codex, Gemini CLI & Copilot.

Install

npx -y skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-chai

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

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Predict biomolecular complexes with Chai-1, an open AlphaFold3-style model that folds multi-entity assemblies (proteins, ligands, nucleic acids) from a single typed FASTA — strong on antibody–antigen and protein–ligand complexes, with optional MSA and restraint inputs. Use when predicting an antibody–antigen complex, folding a mixed protein/ligand/nucleic-acid assembly described in one FASTA, or generating a complex with experimental restraints. For binding-affinity prediction or a ligand-focused co-fold prefer alterlab-boltz; for protein-only or protein–protein folding prefer alterlab-alphafold; to dock into a fixed receptor prefer alterlab-diffdock. Part of the AlterLab Academic Skills suite.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Chai-1 (open complex prediction)

Overview

Chai-1 (Chai Discovery 2024; chaidiscovery/chai-lab) is an open AlphaFold3-style model that predicts multi-entity biomolecular complexes — proteins, small-molecule ligands, and nucleic acids together — from a single typed FASTA. It is particularly used for antibody–antigen and protein–ligand complexes, can run with or without MSAs, and accepts restraints to guide the prediction.

Its niche relative to the other folders: one FASTA describing a mixed assembly, and antibody–antigen in particular. For a ligand co-fold where you specifically want a binding affinity, use alterlab-boltz; for a bare protein, use alterlab-alphafold.

When to Use This Skill

Use this skill when the user wants to:

Predict an antibody–antigen complex structure.
Fold a mixed assembly (protein + ligand + nucleic acid) described in one FASTA.
Run complex prediction with or without MSAs, optionally guided by restraints.
Get an open AlphaFold3-style complex prediction with per-entity confidence.

Does NOT Trigger

Scenario	Use instead
Predict a protein–ligand binding affinity	`alterlab-boltz`
Protein-only or protein–protein folding	`alterlab-alphafold`
Dock a ligand into a fixed receptor structure	`alterlab-diffdock`
Look up an experimental complex structure	`alterlab-pdb`
Design antibody/interface sequences	`alterlab-proteinmpnn` / `alterlab-ligandmpnn`

Core Capabilities

1. Single-FASTA multi-entity input

Chai-1 reads one FASTA whose records are typed by entity. A protein + ligand example:

>protein|antibody-Fv
EVQ...SS
>protein|antigen
MKT...GG
>ligand|cofactor
CC(=O)Oc1ccccc1C(=O)O

# CLI form (verify against installed chai-lab — TODO(verify))
chai-lab fold input.fasta out/

The header type tags (protein, ligand, rna, dna) tell Chai how to treat each record; confirm the exact header/type syntax against your installed version.

2. Antibody–antigen complexes

The common use case: fold an antibody Fv/Fab against its antigen and read the interface confidence (per-model / interface score) to judge whether the predicted epitope/paratope contact is trustworthy. Use restraints when you have partial epitope knowledge.

3. MSA and restraints

MSA optional — Chai-1 can run single-sequence or with MSAs; MSAs generally improve accuracy but cost time. Disclose any hosted-MSA usage for sensitive sequences.
Restraints — supply contact/pocket restraints to bias the prediction toward known biology. TODO(verify) the restraint file format per version.

4. Confidence and GPU dispatch

Read per-entity confidence and the interface score to pick a model. Chai-1 needs a CUDA GPU and caches weights on first run; batch predictions (e.g. an antibody panel against one antigen) via alterlab-remote-compute (submit → poll → harvest out/).

Resources

references/chai_usage.md — install/pinning, FASTA type-tag syntax, MSA/restraint options, outputs, and folder-choice guidance. Loaded on demand.

Part of the AlterLab Academic Skills suite.