CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models.

ABSTRACT:

Motivation

CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues missed by state-of-the-art hidden Markov model (HMM)-based approaches. The method developed (CATHe) combines a neural network with sequence representations obtained from protein language models. It was assessed using a dataset of remote homologues having less than 20% sequence identity to any domain in the training set.

Results

The CATHe models trained on 1773 largest and 50 largest CATH superfamilies had an accuracy of 85.6 ± 0.4% and 98.2 ± 0.3

SUBMITTER: Nallapareddy V 

PROVIDER: S-EPMC9887088 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature