Agent skill · nvidia
msa-structure-prediction-pipeline
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.
What it needs
About 3k tokens when loaded.
What this skill does
MSA Structure Prediction Pipeline Predict protein structures with high accuracy by chaining two BioNeMo NIMs: --- 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 --- Step 2: Predict structure with OpenFold3 Pass the MSA directly into OpenFold3's msa field: --- 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: A larger, higher-quality MSA typically yields higher pLDDT and lower pDE, especially for proteins with many known homologs. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-msa-structure-prediction-pipeline