Detection of circular permutations by Protein Language Models.

ABSTRACT: Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and often treat proteins as rigid bodies. Protein Language Model-based alignment tools have shown advantages in utilizing sequence information to overcome the challenges of detecting distant homologs without requiring structural input. However, many current Protein Language Model-based alignment methods, which rely on sequence alignment algorithms like the Smith-Waterman algorithm, face significant difficulties when dealing with circular permutation (CP) due to their dependency on linear sequence orde

SUBMITTER: Hu Y 

PROVIDER: S-EPMC11757225 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature