msa-structure-prediction-pipeline

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)

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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.

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Before you start

Confirm with the user:

1. Query sequence: amino acid sequence to predict
2. MSA depth: how many sequences to retrieve (default 500; more = slower but more context)
3. 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.

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Step 1: Search for MSA with MSA-Search

import requests, json, os
from pathlib import Path

NGC_API_KEY = os.environ["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")

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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}")

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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.

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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,
}

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Quick reference — skill dependencies