Openfold2 Nim

OpenFold2 NIM

Predict a single protein-chain structure from an amino-acid sequence, with optional A3M multiple sequence alignments and mmCIF templates. Use this SKILL.md 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.

Choose Mode

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

• Hosted URL: https://health.api.nvidia.com/v1/biology/openfold/openfold2/predict-structure-from-msa-and-template
• Local URL: http://localhost:8000/biology/openfold/openfold2/predict-structure-from-msa-and-template
• Local readiness: http://localhost:8000/v1/health/ready

Mode 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.

Auth And Environment

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.

Local Docker

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:

until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

Request Pattern

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.

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()

Payload gotchas:

• OpenFold2 is monomer-only. For protein-ligand, protein-DNA/RNA, or multi-chain complexes, use OpenFold3 or Boltz2 instead.
• sequence must use valid amino-acid IUPAC symbols.
• Hosted API docs list sequence length 1-1000; local docs say current NIM supports sequences up to 2048 residues on supported hardware.
• A3M alignments go under 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.
• Starting with OpenFold2 2.0.0, use 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.

Save And Interpret Output

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.

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)")

For production monomer runs:

• Use selected_models: [1, 2, 3, 4, 5] unless the user requests a smoke test.
• Use relax_prediction: True in Python payloads when relaxation is desired; JSON examples may show true.
• State the sequence length caveat: hosted API docs list 1-1000 residues, while local support-matrix docs list up to 2048 residues on supported hardware.
• If the task is a complex rather than a monomer, redirect to OpenFold3 or Boltz2.

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.

Troubleshooting

• 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.
• Local 404: remove /v1/ from the prediction URL.
• Weak structures: use MSA Search to generate deeper A3M alignments and add biologically relevant mmCIF templates when appropriate.
• Local startup stalls: first run downloads parameters into LOCAL_NIM_CACHE.