PreprintCited by 140Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

Exploring structural diversity across the protein universe with The Encyclopedia of Domains

Open access
Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., Jones, D. T.·2024·Science

Preprint — 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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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)
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
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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