The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
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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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302 · cited 3×
“E) PSI-BLAST alignment of the Duf371 repeats detected with Mefer0473 sequence supports the duplication event, with sequence similarities indicated between N-terminal and C-terminal halves.”
MethodInfluentialCytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · cited 2×
“In Cytoscape [26] display of SCOP domains and high-scoring links between them, numerous links with HHsearch probability above 90% exist between SAM MTases and Rossmann domains.”
BackgroundMethodMUSCLE: multiple sequence alignment with high accuracy and high throughput
Edgar, R. C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh340 · Cited by 44,662 · cited 1×
“75 domains were automatically mapped into the ECOD hierarchy using MUSCLE alignments [27].”
MethodBerman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
BLAST+: architecture and applications
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-421 · Cited by 22,074 · cited 5×
“Domains from the input chains could be classified by any combination of the three sequence-based methods (chain BLAST, domain BLAST, or domain HHsearch).”
MethodInfluentialThe Pfam Protein Families Database
Bateman, A. G. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.276 · Cited by 14,075
Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80134-2 · Cited by 6,435 · cited 1×
“Currently, the two leading structure classifications are SCOP (Structural Classification of Proteins) [4] and CATH (Class, Architecture, Topology, Homology) [5], both of which are widely used in analyzing protein sequence, structure, function, and evolution and in developing various bioinformatics tools.”
BackgroundProtein Structure Comparison by Alignment of Distance Matrices
Holm, L., & Sander, C. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1489 · Cited by 3,984 · cited 4×
“In order to extend distant evolutionary relationships beyond the SCOP superfamily level in ECOD, we apply state of the art homology-inference algorithms both developed in our group [19,20] as well as by others [21,22], manually analyze and verify the suggested homologous links, and incorporate findings from literature.”
MethodInfluentialThe HHpred interactive server for protein homology detection and structure prediction
Söding, J., Biegert, A., & Lupas, A. N. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki408 · Cited by 3,775
TM-align: a protein structure alignment algorithm based on the TM-score
Zhang, Y. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki524 · Cited by 3,774 · cited 1×
“similarity and TMalign to reflect structure similarity [21,40].”
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Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Self-citationThe Pfam protein families database in 2019
El-Gebali, S., Mistry, J., Bateman, A., Eddy, S. R., Luciani, A., Potter, S. C., Qureshi, M., Richardson, L. J., Salazar, G. A., Smart, A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky995 · Cited by 4,602
“However, the majority of the recent additional relationships between Pfam entries have been identified by comparison to the Evolutionary Classification of Protein Domains (ECOD) database (13).”
BackgroundA Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core
Zimmermann, L., Stephens, A., Nam, S.-Z., Rau, D., Kübler, J., Lozajic, M., Gabler, F., Söding, J., Lupas, A. N., & Alva, V. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.12.007 · Cited by 2,522
A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127
Harnessing protein folding neural networks for peptide–protein docking
Tsaban, T., Varga, J. K., Avraham, O., Ben-Aharon, Z., Khramushin, A., & Schueler-Furman, O. (2022) · Nature Communications · doi:10.1038/s41467-021-27838-9 · Cited by 1,205
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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