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ECOD: An Evolutionary Classification of Protein Domains

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...

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Autores principales: Cheng, Hua, Schaeffer, R. Dustin, Liao, Yuxing, Kinch, Lisa N., Pei, Jimin, Shi, Shuoyong, Kim, Bong-Hyun, Grishin, Nick V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256011/
https://www.ncbi.nlm.nih.gov/pubmed/25474468
http://dx.doi.org/10.1371/journal.pcbi.1003926
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author Cheng, Hua
Schaeffer, R. Dustin
Liao, Yuxing
Kinch, Lisa N.
Pei, Jimin
Shi, Shuoyong
Kim, Bong-Hyun
Grishin, Nick V.
author_facet Cheng, Hua
Schaeffer, R. Dustin
Liao, Yuxing
Kinch, Lisa N.
Pei, Jimin
Shi, Shuoyong
Kim, Bong-Hyun
Grishin, Nick V.
author_sort Cheng, Hua
collection PubMed
description 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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spelling pubmed-42560112014-12-11 ECOD: An Evolutionary Classification of Protein Domains Cheng, Hua Schaeffer, R. Dustin Liao, Yuxing Kinch, Lisa N. Pei, Jimin Shi, Shuoyong Kim, Bong-Hyun Grishin, Nick V. PLoS Comput Biol Research Article 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. Public Library of Science 2014-12-04 /pmc/articles/PMC4256011/ /pubmed/25474468 http://dx.doi.org/10.1371/journal.pcbi.1003926 Text en © 2014 Cheng et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Cheng, Hua
Schaeffer, R. Dustin
Liao, Yuxing
Kinch, Lisa N.
Pei, Jimin
Shi, Shuoyong
Kim, Bong-Hyun
Grishin, Nick V.
ECOD: An Evolutionary Classification of Protein Domains
title ECOD: An Evolutionary Classification of Protein Domains
title_full ECOD: An Evolutionary Classification of Protein Domains
title_fullStr ECOD: An Evolutionary Classification of Protein Domains
title_full_unstemmed ECOD: An Evolutionary Classification of Protein Domains
title_short ECOD: An Evolutionary Classification of Protein Domains
title_sort ecod: an evolutionary classification of protein domains
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256011/
https://www.ncbi.nlm.nih.gov/pubmed/25474468
http://dx.doi.org/10.1371/journal.pcbi.1003926
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