Alterlab Chai

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

Core Capabilities

1. Single-FASTA multi-entity input

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

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

bashCopy code
chai-lab fold input.fasta out/                          # single-sequence, fastest
chai-lab fold --use-msa-server --use-templates-server input.fasta out/   # recommended

Each header is <entity_type>|name=<unique label> (the bare type|label form also parses). Valid entity types: protein, ligand (SMILES), rna, dna, glycan. Names must be unique — a repeated name raises. The output directory must be empty.

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, templates and restraints

• MSA optional — Chai-1 runs single-sequence by default; --use-msa-server fetches MSAs from the shared ColabFold MMseqs2 server (discloseable for sensitive sequences) and generally improves accuracy at a time cost. Local MSAs are supplied as aligned.pqt files (convert a3m with chai a3m-to-pqt).
• Templates — --use-templates-server, or your own m8 hit table plus CIFs in CHAI_TEMPLATE_CIF_FOLDER.
• Restraints — a CSV passed as constraint_path, one row per restraint with columns restraint_id, chainA, res_idxA, chainB, res_idxB, connection_type, confidence, min_distance_angstrom, max_distance_angstrom, comment. connection_type is contact (residue↔residue) or pocket (chain↔residue, so res_idxA is left blank). Residue indices are the residue letter plus its 1-based position (D4), and chains are lettered A–Z in input order. Published example: two ground-truth contacts lifted antibody–antigen interface DockQ from ~0.02 to ~0.4 on PDB 7SYZ.

4. Confidence and GPU dispatch

A run writes pred.model_idx_{0..4}.cif plus scores.model_idx_N.npz holding aggregate_score (the ranking number), ptm, iptm, per_chain_ptm, per_chain_pair_iptm, has_inter_chain_clashes and chain_chain_clashes. For an antibody–antigen job the pair-wise per_chain_pair_iptm entry for the two chains — not the global score — is what tells you whether the predicted epitope is trustworthy; check the clash flags before believing a high score.

The Python entry point is chai_lab.chai1.run_inference(fasta_file=..., output_dir=..., num_diffn_samples=5, num_trunk_recycles=3, seed=..., device="cuda:0"), returning a StructureCandidates with cif_paths and ranking_data. Batch 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.