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March 26, 2026Nature Methods6 citationsOpen Access

Clustering the protein universe of life using DIAMOND DeepClust

BBBenjamin BuchfinkMax Planck Institute for BiologyEBEmile BarbeÉcole Polytechnique Fédérale de LausanneHAHaim AshkenazyMax Planck Institute for the History of Science

Key Points

  • To improve the organization of billions of protein sequences for better structural predictions.
  • Developed a new clustering algorithm called DIAMOND DeepClust.
  • Processed 19 billion biosphere proteins to create clusters.
  • Facilitated access to a database for enhanced predictive modeling.
  • Aggregated proteins into 544 million nonsingleton clusters.
  • Demonstrated scalability to trillions of sequences.
  • Enhanced protein structure prediction accuracy with AlphaFold2.

Abstract

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.

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Cite This Study

Buchfink et al. (2026) studied this question.

synapsesocial.com/papers/69c4ccaffdc3bde4489181fahttps://doi.org/10.1038/s41592-026-03030-z
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