Release Notes#

Release 1.0.0#

Summary#

This is the first release of MSA Search NIM. MSA Search enables accurate protein structure prediction from an input protein sequence by predicting potential structural similarities with previously-observed proteins.

This NIM is not a deep learning model but makes use of NVIDIA GPUs and software libraries for accelerated multiple sequence alignment. The NIM relies on GPU-accelerated MMSeqs2 to provide fast, accurate sequence database search and multiple sequence alignment.

  • Supported search_types: alphafold2 and colabfold. Note: Different search types require different database types.

  • Supported NVIDIA GPUs: At least 48GB of GPU Memory (A100, H100, and L40s). Currently there is no official support for GPUs with less than 48 GB of GPU Memory.

Model Variants#

  • ColabFold, which contains the databases Uniref30_2302, colabfold_envdb_202108, and PDB70_220313.

  • AlphaFold, which contains the databases uniref90, mgnify, and smallbfd, as used in AlphaFold2.

Notes and Limitations#

Ensure you use this NIM with GPUs with at least 48 GB of VRAM. In addition, this NIM requires roughly 1.3 Terabytes (1300 Gigabytes) of fast NVMe SSD storage to store the databases.

Note: While there are many options for tuning this NIM’s performance, for most users the defaults will provide a balanced performance experience.

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