CIRPIN: Learning Circular Permutation-Invariant Representations to Uncover Putative Protein Homologs.

ABSTRACT: Protein structure-based homology detection has been revolutionized by deep learning methods that can rapidly search massive databases. However, current structural search tools often miss proteins related by topological rearrangements, particularly circular permutation (CP), where proteins share identical global folds but differ in the positioning of their termini. We introduce a circular permutation-invariant graph neural network (CIRPIN) that addresses this limitation through a novel data augmentation strategy using synthetic circular permutations (synCPs). We demonstrate that CIRPIN learns representations of proteins that are invariant to circular permutation, enabling it to identify similar proteins within the Structural Classification of Proteins - extended (SCOPe) and AlphaFold Cluste

SUBMITTER: Kolodziej AR 

PROVIDER: S-EPMC12667903 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature