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.