Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
This skill wraps AlphaFold2 and AlphaFold2-Multimer through colabfold_batch,
which replaces DeepMind's local-database MSA pipeline with a call to the public
MMseqs2 server — so a prediction is one command and one FASTA, not a 2 TB
database mount. AF2 remains the reference monomer predictor and the multimer
model is still a strong protein–protein validator, but it does not handle
ligands or nucleic acids; for those, route to boltz, chai1, or openfold3.
The ColabFold code is MIT (github.com/sokrypton/ColabFold) and the AlphaFold2
code is Apache-2.0 (github.com/google-deepmind/alphafold); the AF2 model
parameters are CC-BY-4.0 with DeepMind's terms of use.
colabfold_batch input.fasta out \
--num-recycle 3 \
--model-type alphafold2_multimer_v3
The input is a plain FASTA. For a complex, put every chain on one sequence
line separated by : — colabfold_batch builds a paired MSA per segment and
runs the multimer model when it sees the colon (so the explicit --model-type alphafold2_multimer_v3 above is belt-and-braces). For monomers omit
--model-type and the colon. --templates and --amber add PDB templates
and OpenMM relaxation respectively; both are off by default and both add
minutes per model.
ColabFold runs all five AF2 model weights by default and ranks them by pLDDT
(pTM/ipTM for multimer), so output per query lands in out/ as five ranked
PDBs <name>_unrelaxed_rank_00{1..5}_*.pdb (b-factor column carries pLDDT)
and a matching <name>_scores_rank_00{N}_*.json with plddt, ptm, and — for
multimer — iptm and the pae matrix. Rank-1 is the model to read first;
ipTM > 0.5 is the usual soft pass for an interface.
colabfold/batch.py hard-sets TF_FORCE_UNIFIED_MEMORY=1 and
XLA_PYTHON_CLIENT_MEM_FRACTION=4.0 on import. Under a gVisor sandbox unified
memory is unsupported, so JAX's device_put loops indefinitely allocating
host RAM during AF2 parameter load — the job appears hung, never errors.
Override both before the import (TF_FORCE_UNIFIED_MEMORY=0, fraction
0.95), or sed-patch the two assignments out of batch.py in the image
build, or the first fold never starts.
colabfold_batch defaults to --msa-mode mmseqs2_uniref_env, which posts your
sequence to api.colabfold.com. That server is a public, rate-limited
resource: the wait dominates short folds and occasionally times out under load.
For campaigns, run the MSA stage once with --msa-only, keep the resulting
.a3m files, and feed the directory back as the input on subsequent runs — the
GPU stage then starts immediately and the server is not hit again.
| You see | It means / do this |
|---|---|
| Job hangs silently during "Running model_1" with host RAM climbing | Unified-memory loop under gVisor — see the gotcha above; override or patch batch.py. |
RESOURCE_EXHAUSTED / OOM during XLA compile | XLA_PYTHON_CLIENT_MEM_FRACTION too high for the GPU — drop below the 0.95 default to 0.9 or so. |
MSA stage hangs at Submitting job | Public MMseqs2 server is rate-limiting — wait, or pre-compute with --msa-only and re-run from the cached .a3m. |
Next: for designed-sequence validation, superpose the rank-1 model onto
the design backbone with US-align and gate on pLDDT/ipTM thresholds; for
ligand-bearing complexes, hand the same chains to boltz or chai1.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.
日本語の概要は準備中です。原文の説明を表示しています。
Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.
日本語の概要は準備中です。原文の説明を表示しています。
Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare reproducible setup instructions and validate a user-managed named software environment on an Open-Science SSH Compute Host, including direct SSH and Slurm hosts. Use when a remote job needs packages, modules, cache variables, or a repeatable activation that the host does not already provide.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to create or manage a Specialist agent or create, revise, publish, or delete a Skill through the conversational `/Customize` entry. Routes Skill work to the internal skill-creator and handles Specialist work through the JavaScript host.agents SDK.
日本語の概要は準備中です。原文の説明を表示しています。
Predict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence model. Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses for a small fragment library, or to get a starting pose for downstream rescoring. DiffDock predicts geometry, not affinity.
日本語の概要は準備中です。原文の説明を表示しています。