Detection of circular permutations by Protein Language Models

TL;DR

This paper introduces plmCP, a novel protein language model-based method that improves the speed and accuracy of detecting circular permutations in proteins, addressing limitations of traditional methods.

Contribution

The paper presents a new language model-based approach for detecting protein circular permutations, significantly enhancing accuracy and efficiency over existing methods.

Findings

plmCP outperforms traditional methods in accuracy

Detection process is faster with plmCP

Acknowledges structural flexibility in proteins

Abstract

Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods, sequence-based or structure-based, struggle with accuracy and computational efficiency, the latter also limited by treating proteins as rigid bodies. The plmCP method, utilizing a protein language model, not only speeds up the detection process but also enhances the accuracy of identifying circular permutations, contributing significantly to protein research and engineering by acknowledging structural flexibility.