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bioRxiv · 10.1101/2025.03.14.643159

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

Litfin, T.·Zhou, Y.·von Itzstein, M.
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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.

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Litfin, T., Zhou, Y., von Itzstein, M.. 2025-03-16. Ultra-fast and highly sensitive protein structure alignment with segment-level representations and block-sparse optimization. https://doi.org/10.1101/2025.03.14.643159

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