MSA-Search
Version: 3
MSA Search NIM supports GPU-accelerated Multiple Sequence Alignment (MSA) of a query amino acid sequence against a set of protein sequence databases. These databases are searched for similar sequences to the query and then the collection of sequences are aligned to establish similar regions even when the proteins have different lengths and motifs. The outputs of the MSA process are used to inform structural prediction models such as AlphaFold2 and OpenFold. This tends to improve structural prediction accuracy because similar sequences often have similar structures. MSA Search is also used by evolutionary biologists to look for homology between protein sequences that may indicate a common evolutionary origin. The MSA NIM implements two search styles. The AlphaFold2 search type was first used in the AlphaFold2 paper in Nature and performs a single-pass search per database. The ColabFold search process in the MSA Search NIM was first introduced in ColabFold and implements a cascaded search of generated profiles, providing even higher sensitivity and generally better throughput. Both methods utilize GPU-accelerated MMSeqs2 for improved accuracy and reduced latency. Combined with AlphaFold2 or OpenFold, the MSA Search NIM enables a sensitive and high-throughput protein structure prediction pipeline.
Intended Use
Primary Use Cases
- Accelerate lead optimization: Researchers can use NIMs to accelerate the lead optimization process by quickly generating and testing multiple molecular structures, enabling them to identify potential leads more efficiently.
- Streamline data analysis: Researchers can use NIMs to analyze large datasets generated during the drug discovery process, such as molecular dynamics simulations or high-throughput screening data, to identify patterns and trends that can inform the development of new drugs.
- Improve collaboration: NIMs can facilitate collaboration among researchers by providing a standardized platform for sharing and integrating AI models, enabling teams to work together more effectively and efficiently.
- Enhance predictive modeling: Researchers can use NIMs to develop and deploy predictive models that can accurately predict the properties and behavior of molecules, such as their binding affinity or toxicity, enabling them to make more informed decisions during the drug development process.
Responsible AI Considerations
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LicenseCustom
Last UpdatedMay 2025
PublisherNvidia