Inigo Barrio-Hernandez1,8, Jingi Yeo2,8, Jürgen Jänes3, Milot Mirdita2, Cameron L. M. Gilchrist2, Tanita Wein4, Mihaly Varadi1, Sameer Velankar1, Pedro Beltrao3,5 & Martin Steinegger2,6,7
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK.
2School of Biological Sciences, Seoul National University, Seoul, South Korea.
3Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
4Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.
5Swiss Institute of Bioinformatics, Lausanne, Switzerland.
6Artificial Intelligence Institute, Seoul National University, Seoul, South Korea.
7Institute of Molecular Biology and Genetics, Seoul National University, Seoul, South Korea.
8These authors contributed equally: Inigo Barrio-Hernandez, Jingi Yeo
Corresponding authors : Correspondence to Pedro Beltrao or Martin Steinegger.
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.