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Classification of protein quaternary structure by functional domain composition
BACKGROUND: The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins a...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1450311/ https://www.ncbi.nlm.nih.gov/pubmed/16584572 http://dx.doi.org/10.1186/1471-2105-7-187 |
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author | Yu, Xiaojing Wang, Chuan Li, Yixue |
author_facet | Yu, Xiaojing Wang, Chuan Li, Yixue |
author_sort | Yu, Xiaojing |
collection | PubMed |
description | BACKGROUND: The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences. RESULTS: To explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm (NNA) was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11% CONCLUSION: Compared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics. |
format | Text |
id | pubmed-1450311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-14503112006-05-01 Classification of protein quaternary structure by functional domain composition Yu, Xiaojing Wang, Chuan Li, Yixue BMC Bioinformatics Research Article BACKGROUND: The number and the arrangement of subunits that form a protein are referred to as quaternary structure. Quaternary structure is an important protein attribute that is closely related to its function. Proteins with quaternary structure are called oligomeric proteins. Oligomeric proteins are involved in various biological processes, such as metabolism, signal transduction, and chromosome replication. Thus, it is highly desirable to develop some computational methods to automatically classify the quaternary structure of proteins from their sequences. RESULTS: To explore this problem, we adopted an approach based on the functional domain composition of proteins. Every protein was represented by a vector calculated from the domains in the PFAM database. The nearest neighbor algorithm (NNA) was used for classifying the quaternary structure of proteins from this information. The jackknife cross-validation test was performed on the non-redundant protein dataset in which the sequence identity was less than 25%. The overall success rate obtained is 75.17%. Additionally, to demonstrate the effectiveness of this method, we predicted the proteins in an independent dataset and achieved an overall success rate of 84.11% CONCLUSION: Compared with the amino acid composition method and Blast, the results indicate that the domain composition approach may be a more effective and promising high-throughput method in dealing with this complicated problem in bioinformatics. BioMed Central 2006-04-04 /pmc/articles/PMC1450311/ /pubmed/16584572 http://dx.doi.org/10.1186/1471-2105-7-187 Text en Copyright © 2006 Yu et al; licensee BioMed Central Ltd. |
spellingShingle | Research Article Yu, Xiaojing Wang, Chuan Li, Yixue Classification of protein quaternary structure by functional domain composition |
title | Classification of protein quaternary structure by functional domain composition |
title_full | Classification of protein quaternary structure by functional domain composition |
title_fullStr | Classification of protein quaternary structure by functional domain composition |
title_full_unstemmed | Classification of protein quaternary structure by functional domain composition |
title_short | Classification of protein quaternary structure by functional domain composition |
title_sort | classification of protein quaternary structure by functional domain composition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1450311/ https://www.ncbi.nlm.nih.gov/pubmed/16584572 http://dx.doi.org/10.1186/1471-2105-7-187 |
work_keys_str_mv | AT yuxiaojing classificationofproteinquaternarystructurebyfunctionaldomaincomposition AT wangchuan classificationofproteinquaternarystructurebyfunctionaldomaincomposition AT liyixue classificationofproteinquaternarystructurebyfunctionaldomaincomposition |