The Multiple Sequence Alignment (MSA) Search NIM enables protein structure prediction models by providing fast, accurate sequence alignments of a query amino acid sequence against large databases of known proteins.
NIM Overview
Description:
The MSA NIM provides multiple sequence alignment (a modular component of a folding model workflow). Multiple Sequence Alignment (https://en.wikipedia.org/wiki/Multiple_sequence_alignment) is a pre-processing step for models such as OpenFold and Alphafold2. The NIM supports both protein and RNA sequence search using GPU-accelerated MMseqs2 and Riboseek.
The container components are ready for commercial use.
Third-Party Community Consideration
The model embedded within this container is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case.
License/Terms of Use:
GOVERNING TERMS: Use of this NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Use of the MSA data is governed by the Creative Commons Attribution 4.0 International License (CC-BY-4.0).
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Deployment Geography
Global
Release Date
build.nvidia.com: 06/25/2026 via build.nvidia.com
NGC: 06/25/2026 via catalog.ngc.nvidia.com
Program Classes:
The NIM contains GPU-accelerated MMseqs2 for protein sequence search. Pre-indexed databases are pulled automatically at NIM startup based on the selected model profile.
| Model Name | Use Case | How to Pull the Model |
|---|---|---|
| MMseqs2 (GPU Server) | Protein multiple sequence alignment search against ColabFold databases | Automatic |
Deployment Details:
The MSA Search NIM is deployed by pulling and running the container in an environment with appropriate credentials. For instructions to pull and run, hardware requirements, and NVIDIA GPU support matrix, see MSA Search NIM Docs.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster inference times compared to CPU-only solutions.
Container Version(s):
MSA Search v2.5.0
Security Common Vulnerabilities and Exposures (CVEs)
Please review the Security Scanning tab on NGC to view the latest security scan results. For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.
NIM Documentation
Visit the documentation page for release documentation, deployment guides and more.
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
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