Clustering the protein universe of life using DIAMOND DeepClust

Relating billions of proteins across the tree of life remains a challenging task for comparative biosphere genomics and artificial intelligence-driven structure prediction. Here we present DIAMOND DeepClust, a cascaded, ultra-fast clustering method enabling planetary-scale organization of protein space, scaling to trillions of sequences while retaining sensitivity at low identity. Aggregating 19 billion biosphere proteins into 544 million nonsingleton clusters, we show that using our DeepClust database, available for download, can enhance structure prediction with AlphaFold2.

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Publication Details

Journal
Nature Methods
Published
2026-03-24
DOI
https://doi.org/10.1038/s41592-026-03030-z
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
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article

Clustering the protein universe of life using DIAMOND DeepClust

Nature Methods
Machine Learning in Bioinformatics
article

Clustering the protein universe of life using DIAMOND DeepClust

article en

Abstract

Relating billions of proteins across the tree of life remains a challenging task for comparative biosphere genomics and artificial intelligence-driven structure prediction. Here we present DIAMOND DeepClust, a cascaded, ultra-fast clustering method enabling planetary-scale organization of protein space, scaling to trillions of sequences while retaining sensitivity at low identity. Aggregating 19 billion biosphere proteins into 544 million nonsingleton clusters, we show that using our DeepClust database, available for download, can enhance structure prediction with AlphaFold2.

Nature Methods
University of Dundee (GB), Max Planck Computing and Data Facility (DE), Max Planck Institute for Biology (DE)
Openalex Percentile: Top 11%
Machine Learning in Bioinformatics
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