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:

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.

---

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"}