Ultra-fast and highly sensitive protein structure alignment with segment-level representations and block-sparse optimization

Abstract

Deep learning models for protein structure prediction have given rise to extreme growth in 3D structure data. As a result, traditional methods for geometric structure alignment are too slow to effectively search modern structure libraries. In this study we introduce SPfast - a fully geometric method for structure-based alignment which accelerates search by more than 2 orders of magnitude while increasing sensitivity by 21% and 5% compared with foldseek and TMalign respectively. Using the significant speed of SPfast to conduct more than 100B pairwise comparisons between bona fide uncharacterized proteins and a large-scale, annotated structure library uncovers new biological insights relating to type III secretion in pathogenic bacteria and identifies novel toxin-antitoxin systems. Putative SPfast-based functional assignments are supported by orthogonal evidence including shared genomic context and high-confidence AlphaFold3 complex modelling.