proteinmpnn-nim
Apps & AutomationRun ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Use for ProteinMPNN, inverse folding, sequence design, backbone redesign, fixed chains/residues, omit_AAs, sampling temperature, soluble model, hosted NVIDIA API, local Docker, PDB input, and multi-FASTA output.
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ProteinMPNN NIM
Design protein sequences for a supplied backbone PDB. Use this SKILL.md for
first-pass hosted/local usage; load supplemental files only when needed:
references/api.md: exact endpoints, schemas, Docker flags, response fields.references/science.md: inverse-folding uses, limits, and validation.references/parameters.md: design controls, fixed positions, sampling.references/validation.md: FASTA, score, and structure checks.references/examples.md: compact hosted/local request patterns.
Choose Mode
Ask only when context is unclear:
Hosted NVIDIA API or local Docker NIM?
- Hosted:
https://health.api.nvidia.com/v1/biology/ipd/proteinmpnn/predict - Local:
http://localhost:8000/biology/ipd/proteinmpnn/predict
Local inference paths do not include /v1/. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker
startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for
registry login, entitlement checks, and first-run model downloads; pass it
into the container with -e NGC_API_KEY. Local inference requests use no
auth header after readiness. Warm-cache key-free startup varies by
image/version and should not be assumed.
Local Docker
For local setup answers, copy the preflight below exactly before docker login,
docker run, readiness, and the no-auth local request. Do not answer with only
a localhost Python request. This NIM's cache mount is unique:
/home/nvs/.cache/nim, not /opt/nim/.cache.
set -a
[ -f .env ] && . ./.env
set +a
if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"
echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin
export NIM_TEST_GPU="${NIM_TEST_GPU:-0}"
mkdir -p "${LOCAL_NIM_CACHE}"
chmod 777 "${LOCAL_NIM_CACHE}"
docker run -it \
--runtime=nvidia \
--gpus "device=${NIM_TEST_GPU}" \
-e NGC_API_KEY \
-v "${LOCAL_NIM_CACHE}:/home/nvs/.cache/nim" \
-p 8000:8000 \
nvcr.io/nim/ipd/proteinmpnn:latest
Readiness:
until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done
Request Pattern
Read PDB content inline; do not send only a file path.
import os
from pathlib import Path
import requests
HOSTED = True
pdb_content = Path("1R42.pdb").read_text()
url = (
"https://health.api.nvidia.com/v1/biology/ipd/proteinmpnn/predict"
if HOSTED else "http://localhost:8000/biology/ipd/proteinmpnn/predict"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
headers["Authorization"] = f"Bearer {os.environ['NGC_API_KEY']}"
payload = {
"input_pdb": pdb_content,
"num_seq_per_target": 10,
"sampling_temp": [0.1],
"use_soluble_model": False,
"ca_only": False,
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()
Common controls:
- Redesign only chain A:
"input_pdb_chains": ["A"]. - Exclude amino acids:
"omit_AAs": ["C"]or"omit_AAs": ["M"]. - Diversity:
"sampling_temp": [0.1, 0.3, 0.5](always a list). - Solubility bias:
"use_soluble_model": True. - Candidate count:
num_seq_per_targetis 1-100.
Save And Report Output
mfasta = result["mfasta"]
Path("designed_sequences.fa").write_text(mfasta)
# Scores correspond to designed sequences. The mfasta may include a native/WT
# row; do not pair that row with generated-sequence scores.
headers = [line for line in mfasta.splitlines() if line.startswith(">")]
designed_headers = [
h for h in headers if "native" not in h.lower() and "wt" not in h.lower()
]
for header, score in zip(designed_headers, result.get("scores", [])):
print(f"{header} score: {score:.4f}")
Validate promising designs by predicting structures with Boltz2 or OpenFold3
and comparing them to the target backbone. For FASTA/score sanity checks, read
references/validation.md.
Limits And Troubleshooting
- Minimum GPU VRAM: about 3 GB.
sampling_tempmust be a list, even for one value.- Empty
mfasta: check non-emptyinput_pdbandnum_seq_per_target >= 1. - PDB parse errors: use valid PDB ATOM records.
- Local URL 404 usually means an accidental
/v1/prefix. - Cache mount error: use
/home/nvs/.cache/niminside the container.