使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/nvidia-bionemo/bionemo-agent-toolkit --skill msa-structure-prediction-pipeline开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
MSA Structure Prediction Pipeline
Predict protein structures with high accuracy by chaining two BioNeMo NIMs:
Step 1: MSA-Search → Step 2: OpenFold3
(Search homologs) (Predict structure with MSA)Overview
Why chain these NIMs?
- MSA-Search finds evolutionary homologs in UniRef30 and ColabFold databases using GPU-accelerated MMSeqs2. The resulting alignment provides crucial evolutionary information.
- OpenFold3 uses the MSA to improve structure prediction accuracy — especially for sequences where no close homolog exists in PDB.
- Running MSA-Search first means OpenFold3 gets the full evolutionary context rather than a single-sequence prediction.
Before you start
Confirm with the user:
- Query sequence: amino acid sequence to predict
- MSA depth: how many sequences to retrieve (default 500; more = slower but more context)
- API mode: hosted or local Docker?
Note: local MSA-Search requires 1.4 TB of database storage — strongly recommend hosted unless the user has that infrastructure.
For local Docker, do not assume MSA-Search and OpenFold3 are both on localhost:8000 concurrently. Run one container at a time and hand off the A3M file, or start each NIM on a distinct host port and set the URLs explicitly.
Step 1: Search for MSA with MSA-Search
import requests, json, os
from pathlib import Path
NGC_API_KEY = os.getenv("NGC_API_KEY")
HOSTED = True
query_sequence = "<YOUR_PROTEIN_SEQUENCE>"
if HOSTED:
msa_url = "https://health.api.nvidia.com/v1/biology/colabfold/msa-search/predict"
headers = {"Content-Type": "application/json",
"Authorization": f"Bearer {NGC_API_KEY}"}
else:
msa_url = "http://localhost:8000/biology/colabfold/msa-search/predict"
headers = {"Content-Type": "application/json"}
payload = {
"sequence": query_sequence,
"databases": ["Uniref30_2302", "colabfold_envdb_202108"],
"e_value": 0.0001,
"output_alignment_formats": ["a3m"],
}
r = requests.post(msa_url, headers=headers, json=payload)
r.raise_for_status()
msa_result = r.json()
# Extract the A3M alignment
a3m_alignment = msa_result["alignments"]["Uniref30_2302"]["a3m"]["alignment"]
# Save for reference
with open("query_msa.a3m", "w") as f:
f.write(a3m_alignment)
# Count sequences in alignment
n_seqs = a3m_alignment.count(">")
print(f"Step 1 complete: found {n_seqs} homologous sequences")
print(f"MSA saved to query_msa.a3m")Step 2: Predict structure with OpenFold3
Pass the MSA directly into OpenFold3's msa field:
if HOSTED:
of3_url = "https://health.api.nvidia.com/v1/biology/openfold/openfold3/predict"
else:
of3_url = "http://localhost:8000/biology/openfold/openfold3/predict"
# Build the OpenFold3 MSA structure from the retrieved alignment
msa_data = {
"uniref30": {
"a3m": {
"alignment": a3m_alignment,
"format": "a3m"
}
}
}
# Optionally also include colabfold_envdb alignment if requested
# env_alignment = msa_result["alignments"]["colabfold_envdb"]["a3m"]["alignment"]
# msa_data["colabfold_env"] = {"a3m": {"alignment": env_alignment, "format": "a3m"}}
payload = {
"inputs": [{
"input_id": "prediction_with_msa",
"output_format": "pdb",
"molecules": [
{
"type": "protein",
"sequence": query_sequence,
"diffusion_samples": 1,
"msa": msa_data
}
]
}]
}
r = requests.post(of3_url, headers=headers, json=payload, timeout=300)
r.raise_for_status()
result = r.json()
output = result["outputs"][0]
for i, sample in enumerate(output["structures_with_scores"]):
fmt = sample["format"]
filename = f"predicted_structure_{i+1}.{fmt}"
with open(filename, "w") as f:
f.write(sample["structure"])
print(f"\nStep 2 complete: {filename} saved")
print(f" Confidence: {sample['confidence_score']:.4f}")
print(f" pLDDT: {sample['complex_plddt_score']:.4f}")
print(f" pTM: {sample['ptm_score']:.4f}")Comparing single-sequence vs MSA-informed prediction
If the user wants to see the impact of MSA, run OpenFold3 twice — once with the full MSA and once with just the query sequence as a minimal alignment:
# Minimal MSA (single sequence — same as no MSA context):
minimal_msa = {
"main": {
"a3m": {
"alignment": f">query\n{query_sequence}",
"format": "a3m"
}
}
}A larger, higher-quality MSA typically yields higher pLDDT and lower pDE, especially for proteins with many known homologs.
For protein complexes
Use the /paired/predict endpoint of MSA-Search to get paired alignments for multi-chain complexes, then pass each chain's alignment into the corresponding molecule's msa field and paired_msa fields:
# Paired MSA search endpoint for complexes:
msa_paired_url = "https://health.api.nvidia.com/v1/biology/colabfold/msa-search/paired/predict"
paired_payload = {
"sequences": [chain_A_sequence, chain_B_sequence],
"e_value": 0.0001,
}Quick reference — skill dependencies
| Step | Skill | Key endpoint |
|---|---|---|
| MSA search | msa-search-nim | /biology/colabfold/msa-search/predict |
| Structure prediction | openfold3-nim | /biology/openfold/openfold3/predict |
