RT Journal Article
SR Electronic
T1 Clustering predicted structures at the scale of the known protein universe
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.03.09.531927
DO 10.1101/2023.03.09.531927
A1 Barrio Hernandez, Inigo
A1 Yeo, Jingi
A1 Jänes, Jürgen
A1 Wein, Tanita
A1 Varadi, Mihaly
A1 Velankar, Sameer
A1 Beltrao, Pedro
A1 Steinegger, Martin
YR 2023
UL http://biorxiv.org/content/early/2023/03/10/2023.03.09.531927.abstract
AB 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 accuracy with over 214 million predicted structures available in the AlphaFold database (AFDB). 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 structures in AFDB, identifying 2.27M non-singleton structural clusters, of which 31% lack annotations representing likely novel structures. Clusters without annotation tend to have few representatives covering only 4% of all proteins in the AFDB. Evolutionary analysis suggests that most clusters are ancient in origin but 4% seem species specific, representing lower quality predictions or examples of de-novo gene birth. Additionally, we show how structural comparisons can be used to predict domain families and their relationships, identifying examples of remote homology. Based on these analyses we identify several examples of human immune related proteins with remote homology in prokaryotic species which illustrates the value of this resource for studying protein function and evolution across the tree of life.Availability Methods and data are available at cluster.foldseek.comCompeting Interest StatementThe authors have declared no competing interest.