CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models
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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 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%, 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 AlphaFold2 structures with structures from the CATH superfamilies to which they were assigned. Availability and implementation: The code for the developed models is available on https://github.com/vam-sin/CATHe, and the datasets developed in this study can be accessed on https://zenodo.org/record/6327572. Supplementary information: Supplementary data are available at Bioinformatics online.
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- Authors
- Nallapareddy, V., Bordin, N., Sillitoe, I., Heinzinger, M., Littmann, M., Waman, V. P., Sen, N., Rost, B., & Orengo, C.
- Affiliations
- Institute of Structural and Molecular Biology (GB)
- Technical University of Munich (DE)
- Corresponding author
- Orengo, Christine
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-01-01
- Volume
- 39
- Issue
- 1
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC9887088
- OpenAlex
- W4316926589
- Cited by
- 47
- References
- 9
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 13 · 2023: 9 · 2022: 6
- FWCI
- 3.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- BBSRC (BB/V014722/1, BB/R009597/1, BB/R014892/1, BB/S020144/1, BB/S020039/1)
- Software Campus 2.0 (01IS17049)
- German Ministry for Research and Education
- Deutsche Forschungsgemeinschaft (DFG–GZ, RO1320/4–1)
- Bavarian Ministry of Education
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein domain classification, protein language models, protein embeddings, neural network classifier, Pfam domains, structural validation, AlphaFold2
- MeSH terms
- Algorithms, Humans, Proteins, Sequence Homology, Amino Acid, Databases, Protein
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