alterlab-alphafold

name	alterlab-alphafold
description	Predict protein 3D structures with AlphaFold2 via ColabFold — MMseqs2-accelerated MSAs, monomer and AlphaFold2-Multimer complex folding, and confidence-based validation (pLDDT, pTM/ipTM, PAE). Use when folding a protein sequence or complex from FASTA, generating a predicted structure with confidence metrics, ranking models, or checking self-consistency of a design. For co-folding a protein WITH a small-molecule ligand or predicting binding affinity prefer alterlab-boltz; for antibody–antigen or one-FASTA multi-entity complexes prefer alterlab-chai; to LOOK UP an already-computed structure prefer alterlab-alphafold-db; for ESM embeddings or inverse folding prefer alterlab-esm. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	Runs via ColabFold (`colabfold_batch`; install `colabfold[alphafold]` — TODO(verify) exact pin) under `uv run python`. Requires a CUDA GPU for folding (JAX/CUDA); the MSA step uses the hosted MMseqs2 API by default or a local database. AF2 network weights are downloaded once and cached (~several GB). Dispatch heavy runs via alterlab-remote-compute.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

AlphaFold (via ColabFold)

Overview

Predict a protein's 3D structure from its amino-acid sequence with AlphaFold2, run through ColabFold (Mirdita et al., Nature Methods 2022) — which replaces AlphaFold's slow genetic-database MSA search with the fast MMseqs2 API, making folding practical on a single GPU. Handles single chains (monomer) and complexes via AlphaFold2-Multimer (Evans et al. 2021), and reports per-residue and per-interface confidence metrics so you know which parts of a prediction to trust.

This skill runs folding and returns structures + confidence. To retrieve an already-computed AlphaFold prediction for a known UniProt entry without running anything, use alterlab-alphafold-db instead.

When to Use This Skill

Use this skill when the user wants to:

• Fold a protein sequence (FASTA) into a predicted 3D structure (PDB/mmCIF).
• Predict a protein complex (AF2-Multimer) and score the interface (ipTM).
• Rank multiple models and read confidence (pLDDT, pTM, PAE) to judge reliability.
• Validate a designed sequence by refolding it and checking self-consistency vs. a target.

Does NOT Trigger

Scenario	Use instead
Co-fold a protein with a ligand (SMILES/CCD) or predict binding affinity	alterlab-boltz
Antibody–antigen / arbitrary multi-entity complex from one FASTA	alterlab-chai
Look up a precomputed AlphaFold model by UniProt id	alterlab-alphafold-db
ESM embeddings, inverse folding, generative design	alterlab-esm
Dock a ligand into an existing structure	alterlab-diffdock
De-novo backbone generation	alterlab-rfdiffusion

Core Capabilities

1. Monomer folding

# One sequence per FASTA record; MSAs via the hosted MMseqs2 API (--msa-mode)
colabfold_batch input.fasta out/ --num-models 5 --num-recycle 3

Outputs per record: ranked *_relaxed_rank_001_*.pdb, a JSON with /, and coverage/pLDDT plots. exact flag names against your installed ColabFold.