Exploring structural diversity across the protein universe with The Encyclopedia of Domains
Open accessPreprint — published as 10.1126/science.adq4946 · Paper page
Abstract
The AlphaFold Protein Structure Database (AFDB) contains full-length predictions of the three-dimensional structures of almost every protein in UniProt. Because protein function is closely linked to structure, the AFDB is poised to revolutionise our understanding of biology, evolution and more. Protein structures are composed of domains, independently folding units that can be found in multiple structural contexts and functional roles. The AFDB’s potential remains untapped due to the difficulty of characterising 200 million structures. Here we present The Encyclopedia of Domains or TED, which combines state-of-the-art deep learning-based domain parsing and structure comparison algorithms to segment and classify domains across the whole AFDB. TED describes over 370 million domains, over 100 million more than detectable by sequence-based methods. Nearly 80% of TED domains share similarities to known superfamilies in CATH, greatly expanding the set of known protein structural domains. We uncover over 10,000 previously unseen structural interactions between superfamilies, expand domain coverage to over 1 million taxa, and unveil thousands of architectures and folds across the unexplored continuum of protein fold space. We expect TED to be a valuable resource that provides a functional interface to the AFDB, empowering it to be useful for a multitude of downstream analyses.
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Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · see citing works
Predicting multiple conformations via sequence clustering and AlphaFold2
Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545 · see citing works
Sampling alternative conformational states of transporters and receptors with AlphaFold2
del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2022) · eLife · doi:10.7554/elife.75751 · Cited by 511 · see citing works
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- Authors
- Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., & Jones, D. T.
- Affiliations
- University College London (GB)
- Institute of Structural and Molecular Biology (GB)
- Corresponding author
- Orengo, C. A.
- Jones, D. T.
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2024-03-19
- Volume
- 386
- Issue
- 6721
- Pages
- eadq4946-eadq4946
- ISSN
- 0036-8075 (ISSN-L)
- PMC
- PMC7618865
- OpenAlex
- W4403925091
- Cited by
- 140
- References
- 3
- Citations by year
- 2026: 71 · 2025: 61 · 2024: 8
- FWCI
- 19.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (221327/Z/20/Z, 221327, 218529/Z/19/Z)
- Alan Turing Institute (218529/Z/19/Z)
- University College London
- Medical Research Council (HDR-23005)
- Biotechnology and Biological Sciences Research Council (BB/W008556/1, BB/W018802/1, BB/Y001117/1, BB/T019409/1)
- Health Data Research UK
- 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
- protein domains, domain detection, domain classification, protein superfamilies, protein fold, structural diversity, deep learning, protein structure prediction
- MeSH terms
- Protein Domains, Deep Learning, Proteins, Protein Folding, Databases, Protein
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