# Msa Structure Prediction Pipeline

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external...

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: Engineering
- License: Apache-2.0 AND CC-BY-4.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NVIDIA/skills --skill msa-structure-prediction-pipeline -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill msa-structure-prediction-pipeline -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill msa-structure-prediction-pipeline -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill msa-structure-prediction-pipeline -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```python
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"}

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/bionemo-msa-structure-prediction-pipeline/SKILL.md