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Documentation
SKILL.md
npx skills add nvidia/skills --skill openfold2-nim
Use this skill for OpenFold2, NVIDIA's BioNeMo NIM microservice for monomer protein structure prediction. Invoke whenever the user mentions OpenFold2, AlphaFold2-like monomer folding, protein sequence-to-structure prediction, A3M MSAs, mmCIF templates, hosted NVIDIA API calls, or local Docker deployment.
npx skills add nvidia/skills --skill openfold2-nim
Predict a single protein-chain structure from an amino-acid sequence, with optional A3M multiple sequence alignments and mmCIF templates. Use this guide for basic hosted/local NIM use; load supplemental files only when the task needs deeper context:
references/api.md: exact endpoints, schemas, Docker flags, response fields.references/science.md: model scope, strengths, limitations, and handoffs.references/parameters.md: MSA, template, model-selection, and relax effects.references/validation.md: artifact and scientific sanity checks.references/examples.md: compact hosted/local payload patterns.Ask only when context is unclear:
Hosted NVIDIA API or local Docker NIM?
https://health.api.nvidia.com/v1/biology/openfold/openfold2/predict-structure-from-msa-and-templatehttp://localhost:8000/biology/openfold/openfold2/predict-structure-from-msa-and-templatehttp://localhost:8000/v1/health/readyMode difference: hosted and local use the same prediction path except local
does not include /v1/. Hosted requests use Authorization: Bearer $NGC_API_KEY; local inference requests use no auth header after readiness.
Do not print API keys. Confirm they exist with shell tests, not echoes.
Hosted needs NGC_API_KEY in the request header. Supported local Docker
startup uses NGC_API_KEY, or NVIDIA_API_KEY as a fallback, plus
LOCAL_NIM_CACHE. A repo-root .env file may be sourced as a local override.
Use the official OpenFold2 NIM image and mount LOCAL_NIM_CACHE at
/opt/nim/.cache. Current docs recommend at least 80 GB disk, 64 GB system
RAM, 8 CPU cores, and one supported GPU; the container is roughly 55 GB and
first startup downloads about 10 GB of model parameters.
For the exact startup preflight (.env sourcing, NGC_API_KEY/NVIDIA_API_KEY
handling, docker login, and the docker run for
nvcr.io/nim/openfold/openfold2:latest), copy the command block in
references/api.md under Local Docker verbatim — do
not drop .env, NGC_API_KEY, LOCAL_NIM_CACHE, or the no-auth local request.
Readiness check:
Use Python requests; curl escaping is fragile for A3M/mmCIF text. The
sequence field is required. input_id, alignments, selected_models,
relax_prediction, use_templates, and explicit_templates are optional.
Payload gotchas:
sequence must use valid amino-acid IUPAC symbols.alignments by database name, then a3m with
alignment and format. When the user needs to create or deepen an MSA,
hand off to msa-search-nim / MSA Search and map its A3M output into this
alignments shape.explicit_templates with mmCIF content;
do not write new HHR-template examples.selected_models chooses AlphaFold2/OpenFold parameter sets 1-5. Select one
or two models for smoke tests; use all five for stronger production runs.The response includes one prediction per selected model, ordered by confidence.
Save every returned structure-like text field and the full JSON response so
field-shape differences are auditable. Production answers should explicitly
write .pdb or .cif artifacts, preserve the response JSON, and print any
confidence/ranking fields the service returns.
For production monomer runs:
selected_models: [1, 2, 3, 4, 5] unless the user requests a smoke test.relax_prediction: True in Python payloads when relaxation is desired;
JSON examples may show true.Treat tiny toy sequences and single-sequence MSAs as API smoke tests, not
quality evidence. For scientific interpretation and validation, read
references/science.md and references/validation.md.
401: missing, expired, or unauthorized NGC API key.422: invalid amino-acid characters, sequence too long, malformed A3M, bad
selected_models, or malformed mmCIF template object.404: remove /v1/ from the prediction URL.LOCAL_NIM_CACHE.import os
import requests
hosted = True
url = (
"https://health.api.nvidia.com/v1/biology/openfold/openfold2/predict-structure-from-msa-and-template"
if hosted
else "http://localhost:8000/biology/openfold/openfold2/predict-structure-from-msa-and-template"
)
headers = {"Content-Type": "application/json"}
if hosted:
headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"
seq = "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPT"
payload = {
"sequence": seq,
"input_id": "kras_fragment",
"selected_models": [1],
"relax_prediction": False,
"alignments": {
"uniref90": {
"a3m": {
"alignment": f">query\n{seq}",
"format": "a3m",
}
}
},
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()from pathlib import Path
import json
Path("openfold2_response.json").write_text(json.dumps(result, indent=2))
def save_strings(obj, prefix="openfold2"):
i = 0
if isinstance(obj, dict):
for key, value in obj.items():
if isinstance(value, str) and ("ATOM" in value or value.lstrip().startswith("data_")):
i += 1
ext = "cif" if value.lstrip().startswith("data_") else "pdb"
Path(f"{prefix}_{key}_{i}.{ext}").write_text(value)
elif isinstance(value, (dict, list)):
i += save_strings(value, f"{prefix}_{key}")
elif isinstance(obj, list):
for idx, value in enumerate(obj, start=1):
if isinstance(value, (dict, list)):
i += save_strings(value, f"{prefix}_{idx}")
return i
saved = save_strings(result)
print(f"saved {saved} structure artifact(s)")