| name | alterlab-chai |
| description | 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. |
| license | Apache-2.0 |
| allowed-tools | Read Write Edit Bash(python:*) Bash(uv:*) |
| compatibility | Runs the Chai-1 model (`chaidiscovery/chai-lab`; install the `chai_lab` package — TODO(verify) exact pin) under `uv run python`. Requires a CUDA GPU; weights download once and cache (several GB). Input is a single FASTA with typed records (protein/ligand/RNA/DNA); MSAs and restraints are optional. Dispatch heavy runs via alterlab-remote-compute. |
| metadata | {"skill-author":"AlterLab","version":"1.0.0"} |
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
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.
