Merizo: a rapid and accurate protein domain segmentation method using invariant point attention
Open accessPublished version of a preprint
Abstract
The AlphaFold Protein Structure Database, containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the database is precluded by an urgent need for tools that allow the efficient traversal, discovery, and documentation of its contents. Identifying domain regions in the database is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on AlphaFold2 models via self-distillation, enabling it to be applied to both experimental and AlphaFold2 models. As proof of concept, we apply Merizo to the human proteome, identifying 40,818 putative domains that can be matched to CATH representative domains.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
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Highly accurate protein structure prediction for the human proteome
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“In this study, we have developed a fast and accurate domain segmentation method which can be applied to both experimentally-derived PDBs as well as in silico models such as those generated by AlphaFold2.”
BackgroundMethodInfluentialFast and accurate protein structure search with Foldseek
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Fast and accurate protein structure search with Foldseek
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Domain size distributions can predict domain boundaries
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Chainsaw: protein domain segmentation with fully convolutional neural networks
Wells, J., Hawkins-Hooker, A., Bordin, N., Sillitoe, I., Paige, B., & Orengo, C. (2023) · doi:10.1101/2023.07.19.549732 · Cited by 53 · see citing works
- Authors
- Lau, A. M., Kandathil, S. M., & Jones, D. T.
- Affiliations
- University College London (GB)
- Corresponding author
- Jones, David T.
- Venue
- Nature Communications
- Published
- 2023-12-19
- Volume
- 14
- Issue
- 1
- Pages
- 8445-8445
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10730818
- OpenAlex
- W4389947479
- Cited by
- 48
- References
- 9
- Citations by year
- 2026: 21 · 2025: 15 · 2024: 11 · 2023: 1
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- Funders
- RCUK | Biotechnology and Biological Sciences Research Council (BB/T019409/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- AlphaFold2, self-distillation, deep learning, human proteome, protein structure
- MeSH terms
- Protein Domains, Humans, Proteins, Protein Structure, Tertiary, Genomics, Databases, Protein
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