Sold by: NVIDIAÂ
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Generates a multiple sequence alignment from a query sequence and a protein sequence database search.
Overview
The MSA search NIM is powered by GPU MMSeqs2. GPU MMSeqs2 is a GPU-accelerated toolkit for protein database search and Multiple Sequence Alignment (MSA). While not a deep learning model, MMSeqs2 does require large protein databases for sequence similarity search.
The MSA Search NIM enables researchers and commercial entities in the Drug Discovery, Life Sciences, and Digital Biology fields to rapidly generate multiple sequence alignments (MSA). The output MSA can be used in downstream protein structure prediction and evolutionary analysis applications.
Highlights
- The MSA Search NIM is based on GPU MMSeqs2, a highly-sensitive, highly-performant, GPU-accelerated package for a variety of sequence-alignment tasks. The NIM accuracy should match that of the open-source version of MMSeqs2 in most settings.
Details
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Latest version
Deployed on AWS
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Pricing
Free trial
Try this product free for 31 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $1.00 |
ml.g6e.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $1.00 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $1.00 |
ml.g6e.48xlarge Inference (Real-Time) | Model inference on the ml.g6e.48xlarge instance type, real-time mode | $1.00 |
ml.p4d.24xlarge Inference (Real-Time) | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $1.00 |
ml.p4de.24xlarge Inference (Real-Time) | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $1.00 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $1.00 |
ml.p5e.48xlarge Inference (Real-Time) | Model inference on the ml.p5e.48xlarge instance type, real-time mode | $1.00 |
ml.p5en.48xlarge Inference (Real-Time) | Model inference on the ml.p5en.48xlarge instance type, real-time mode | $1.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters on /invocations and /ping APIs that can be used to control the generated text. See examples and field descriptions below.
- Input MIME type
- application/json
payload = { "sequence": ( "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN" ), "e_value": 0.0001, "iterations": 2, "databases": ["Uniref30_2302", "colabfold_envdb_202108", "PDB70_220313"], }
payload = { "sequence": ( "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN" ), "e_value": 0.0001, "iterations": 2, "databases": ["Uniref30_2302", "colabfold_envdb_202108", "PDB70_220313"], }
Resources
Support
Vendor support
Free support via NVIDIA NIM Developer Forum:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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The Steinegger Lab Dataset comprises biological databases and resources critical for protein sequence and structure analysis, developed to support ColabFold, MMseqs2, and Foldseek/Foldcomp—three high-performance computational tools widely used in bioinformatics.
The MMseqs2 dataset serves as the backbone for our fast structure prediction tool, ColabFold, and includes UniRef30, BFD, and the ColabFold environmental databases.
These datasets are specifically designed for the rapid generation of multiple sequence alignments (MSAs), which are essential for high-accuracy structure prediction.
Beyond MSA generation, these resources allow for fast taxonomy annotations and functional annotation, supporting a wide range of bioinformatics applications.
The Foldseek dataset includes preprocessed databases such as the AlphaFold Database (AFDB), PDB, SwissProt, and CATH, specifically designed for protein structure similarity searches.
These datasets encompass the majority of both experimental[...]
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