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Protein domain identification methods and online resources
Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decad...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895673/ https://www.ncbi.nlm.nih.gov/pubmed/33680357 http://dx.doi.org/10.1016/j.csbj.2021.01.041 |
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author | Wang, Yan Zhang, Hang Zhong, Haolin Xue, Zhidong |
author_facet | Wang, Yan Zhang, Hang Zhong, Haolin Xue, Zhidong |
author_sort | Wang, Yan |
collection | PubMed |
description | Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods. |
format | Online Article Text |
id | pubmed-7895673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-78956732021-03-04 Protein domain identification methods and online resources Wang, Yan Zhang, Hang Zhong, Haolin Xue, Zhidong Comput Struct Biotechnol J Review Article Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods. Research Network of Computational and Structural Biotechnology 2021-02-02 /pmc/articles/PMC7895673/ /pubmed/33680357 http://dx.doi.org/10.1016/j.csbj.2021.01.041 Text en © 2021 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review Article Wang, Yan Zhang, Hang Zhong, Haolin Xue, Zhidong Protein domain identification methods and online resources |
title | Protein domain identification methods and online resources |
title_full | Protein domain identification methods and online resources |
title_fullStr | Protein domain identification methods and online resources |
title_full_unstemmed | Protein domain identification methods and online resources |
title_short | Protein domain identification methods and online resources |
title_sort | protein domain identification methods and online resources |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895673/ https://www.ncbi.nlm.nih.gov/pubmed/33680357 http://dx.doi.org/10.1016/j.csbj.2021.01.041 |
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