Unsupervised domain classification of AlphaFold2-predicted protein structures

Abstract

AbstractThe release of the AlphaFold database, which contains 214 million predicted protein structures, represents a major leap forward for proteomics and its applications. However, lack of comprehensive protein annotation limits its accessibility and usability. Here, we present DPCstruct, an unsupervised clustering algorithm designed to provide domain-level classification of protein structures. Using structural predictions from AlphaFold2 and comprehensive all-against-all local alignments from Foldseek, DPCstruct identifies and groups recurrent structural motifs into domain clusters. When applied to the Foldseek Cluster database, a representative set of proteins from the AlphaFoldDB, DPCstruct successfully recovers the majority of protein folds catalogued in established databases such as SCOP and CATH. Out of the 28,246 clusters identified by DPCstruct, 24% have no structural or sequence similarity to known protein families. Supported by a modular and efficient implementation, classifying 15 million entries in less than 48 hours, DPCstruct is well suited for large-scale proteomics and metagenomics applications. It also facilitates the rapid incorporation of updates from the latest structural prediction tools, ensuring that the classification remains up-to-date. The DPCstruct pipeline and associated database are freely available in a dedicated repository, enhancing the navigation of the AlphaFoldDB through domain annotations and enabling rapid classification of other protein datasets.