Clustering predicted structures at the scale of the known protein universe

Inigo Barrio-Hernandez, Jingi Yeo, Jürgen Jänes, Milot Mirdita, Cameron L. M. Gilchrist, Tanita Wein, Mihaly Varadi, Sameer Velankar, Pedro Beltrao () and Martin Steinegger ()
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Inigo Barrio-Hernandez: Wellcome Genome Campus
Jingi Yeo: Seoul National University
Jürgen Jänes: ETH Zurich
Milot Mirdita: Seoul National University
Cameron L. M. Gilchrist: Seoul National University
Tanita Wein: Weizmann Institute of Science
Mihaly Varadi: Wellcome Genome Campus
Sameer Velankar: Wellcome Genome Campus
Pedro Beltrao: ETH Zurich
Martin Steinegger: Seoul National University

Nature, 2023, vol. 622, issue 7983, 637-645

Abstract: Abstract Proteins are key to all cellular processes and their structure is important in understanding their function and evolution. Sequence-based predictions of protein structures have increased in accuracy1, and over 214 million predicted structures are available in the AlphaFold database2. However, studying protein structures at this scale requires highly efficient methods. Here, we developed a structural-alignment-based clustering algorithm—Foldseek cluster—that can cluster hundreds of millions of structures. Using this method, we have clustered all of the structures in the AlphaFold database, identifying 2.30 million non-singleton structural clusters, of which 31% lack annotations representing probable previously undescribed structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AlphaFold database. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem to be species specific, representing lower-quality predictions or examples of de novo gene birth. We also show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote structural similarity. On the basis of these analyses, we identify several examples of human immune-related proteins with putative remote homology in prokaryotic species, illustrating the value of this resource for studying protein function and evolution across the tree of life.

Date: 2023
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DOI: 10.1038/s41586-023-06510-w

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