Predict structures for single proteins or protein complexes with AlphaFold2 through ColabFold. Use confidence scores and comparisons with a designed structure to assess whether a designed sequence folds as intended.
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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.
安装前可以查看交给 AI 代理的指令内容。
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.
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Use Boltz-2 to predict structures of complexes containing proteins, DNA, RNA, and small molecules. Obtain structure files and confidence scores to assess designed binders, with optional predictions of binding strength between a protein and a small molecule.
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Predict patterns of gene expression and other genomic measurements from DNA sequences with Borzoi. Compare reference and altered sequences to estimate regulatory effects, generate predicted coverage tracks, and prioritize variants in regions that do not encode proteins.
中文简介正在准备中。当前显示英语简介。
Predict three-dimensional structures of complexes containing proteins, RNA, DNA, and small molecules with Chai-1. Compare candidate structures using confidence scores and clash indicators, or run repeated predictions from Python to evaluate designed binders.
中文简介正在准备中。当前显示英语简介。
Prepare reproducible setup and removal instructions for software environments on Open-Science SSH hosts, including Slurm clusters. Check available tools, define named activation settings, and validate the environment where jobs run after the user installs it.
中文简介正在准备中。当前显示英语简介。
Create and manage focused Specialist agents and Skills through conversation. Draft agent roles, review available Skills and Connectors, configure access, and manage changes, with Skill creation and publishing handled by the application's internal skill-creator.
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Predict how a small molecule may sit within a protein using structural inputs. Generate ranked 3D poses without defining a search region, then use those poses as starting points for further evaluation.
中文简介正在准备中。当前显示英语简介。