Nallapareddy, Vamsi;
Bordin, Nicola;
Sillitoe, Ian;
Heinzinger, Michael;
Littmann, Maria;
Waman, Vaishali P;
Sen, Neeladri;
... Orengo, Christine; + view all
(2023)
CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models.
Bioinformatics
, 39
(1)
, Article btad029. 10.1093/bioinformatics/btad029.
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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 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% respectively. As a further test of the power of CATHe to detect more remote homologues missed by HMMs derived from CATH domains, we used a dataset consisting of protein domains that had annotations in Pfam, but not in CATH. By using highly reliable CATHe predictions (expected error rate <0.5%), we were able to provide CATH annotations for 4.62 million Pfam domains. For a subset of these domains from Homo sapiens, we structurally validated 90.86% of the predictions by comparing their corresponding AlphaFold 2 structures with structures from the CATH superfamilies to which they were assigned. AVAILABILITY AND IMPLEMENTATION: The code for the developed models can be found on https://github.com/vam-sin/CATHe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
| Type: | Article |
|---|---|
| Title: | CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/bioinformatics/btad029 |
| Publisher version: | https://doi.org/10.1093/bioinformatics/btad029 |
| Language: | English |
| Additional information: | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163631 |
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