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ECOD: An Evolutionary Classification of Protein Domains
Open accessAbstract
Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or "fold"). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies.
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“The ECOD homology level (“H level”) is a strict similarity criterion since it clusters domains even if they only have distant relationships.”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [37]
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“we used the ECOD classifier [37] (file: “ecod.develop277.domains.txt”, version: “develop277”) to remove the similar complexes and ensure the training complexes share no ECOD homology domain with the benchmark datasets.”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [37]
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Metadata
- Authors
- Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., Shi, S., Kim, B.-H., & Grishin, N. V.
- Affiliations
- Howard Hughes Medical Institute (US)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Grishin, Nick V.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-12-04
- Volume
- 10
- Issue
- 12
- Pages
- e1003926
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4256011
- OpenAlex
- W1965827393
- Cited by
- 426
- References
- 61
- Citations by year
- 2026: 34 · 2025: 48 · 2024: 62 · 2023: 46 · 2022: 46 · 2021: 46 · 2020: 31 · 2019: 24 · 2018: 40 · 2017: 20 · 2016: 14 · 2015: 14
- FWCI
- 6.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Howard Hughes Medical Institute
- Welch Foundation (I-505)
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM094575, GM-094575)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- protein domain evolution, protein structure classification, homology, distant homologs, protein structure evolution, sequence families, PDB structures, protein domains, online databases
- MeSH terms
- Models, Molecular, Proteins, Protein Structure, Tertiary, Evolution, Molecular, Computational Biology, Databases, Protein
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