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iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix
Nuclear receptors (NRs) form a family of ligand-activated transcription factors that regulate a wide variety of biological processes, such as homeostasis, reproduction, development, and metabolism. Human genome contains 48 genes encoding NRs. These receptors have become one of the most important tar...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3283608/ https://www.ncbi.nlm.nih.gov/pubmed/22363503 http://dx.doi.org/10.1371/journal.pone.0030869 |
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author | Xiao, Xuan Wang, Pu Chou, Kuo-Chen |
author_facet | Xiao, Xuan Wang, Pu Chou, Kuo-Chen |
author_sort | Xiao, Xuan |
collection | PubMed |
description | Nuclear receptors (NRs) form a family of ligand-activated transcription factors that regulate a wide variety of biological processes, such as homeostasis, reproduction, development, and metabolism. Human genome contains 48 genes encoding NRs. These receptors have become one of the most important targets for therapeutic drug development. According to their different action mechanisms or functions, NRs have been classified into seven subfamilies. With the avalanche of protein sequences generated in the postgenomic age, we are facing the following challenging problems. Given an uncharacterized protein sequence, how can we identify whether it is a nuclear receptor? If it is, what subfamily it belongs to? To address these problems, we developed a predictor called iNR-PhysChem in which the protein samples were expressed by a novel mode of pseudo amino acid composition (PseAAC) whose components were derived from a physical-chemical matrix via a series of auto-covariance and cross-covariance transformations. It was observed that the overall success rate achieved by iNR-PhysChem was over 98% in identifying NRs or non-NRs, and over 92% in identifying NRs among the following seven subfamilies: NR1[Image: see text]thyroid hormone like, NR2[Image: see text]HNF4-like, NR3[Image: see text]estrogen like, NR4[Image: see text]nerve growth factor IB-like, NR5[Image: see text]fushi tarazu-F1 like, NR6[Image: see text]germ cell nuclear factor like, and NR0[Image: see text]knirps like. These rates were derived by the jackknife tests on a stringent benchmark dataset in which none of protein sequences included has [Image: see text] pairwise sequence identity to any other in a same subset. As a user-friendly web-server, iNR-PhysChem is freely accessible to the public at either http://www.jci-bioinfo.cn/iNR-PhysChem or http://icpr.jci.edu.cn/bioinfo/iNR-PhysChem. Also a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics involved in developing the predictor. It is anticipated that iNR-PhysChem may become a useful high throughput tool for both basic research and drug design. |
format | Online Article Text |
id | pubmed-3283608 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-32836082012-02-23 iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix Xiao, Xuan Wang, Pu Chou, Kuo-Chen PLoS One Research Article Nuclear receptors (NRs) form a family of ligand-activated transcription factors that regulate a wide variety of biological processes, such as homeostasis, reproduction, development, and metabolism. Human genome contains 48 genes encoding NRs. These receptors have become one of the most important targets for therapeutic drug development. According to their different action mechanisms or functions, NRs have been classified into seven subfamilies. With the avalanche of protein sequences generated in the postgenomic age, we are facing the following challenging problems. Given an uncharacterized protein sequence, how can we identify whether it is a nuclear receptor? If it is, what subfamily it belongs to? To address these problems, we developed a predictor called iNR-PhysChem in which the protein samples were expressed by a novel mode of pseudo amino acid composition (PseAAC) whose components were derived from a physical-chemical matrix via a series of auto-covariance and cross-covariance transformations. It was observed that the overall success rate achieved by iNR-PhysChem was over 98% in identifying NRs or non-NRs, and over 92% in identifying NRs among the following seven subfamilies: NR1[Image: see text]thyroid hormone like, NR2[Image: see text]HNF4-like, NR3[Image: see text]estrogen like, NR4[Image: see text]nerve growth factor IB-like, NR5[Image: see text]fushi tarazu-F1 like, NR6[Image: see text]germ cell nuclear factor like, and NR0[Image: see text]knirps like. These rates were derived by the jackknife tests on a stringent benchmark dataset in which none of protein sequences included has [Image: see text] pairwise sequence identity to any other in a same subset. As a user-friendly web-server, iNR-PhysChem is freely accessible to the public at either http://www.jci-bioinfo.cn/iNR-PhysChem or http://icpr.jci.edu.cn/bioinfo/iNR-PhysChem. Also a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics involved in developing the predictor. It is anticipated that iNR-PhysChem may become a useful high throughput tool for both basic research and drug design. Public Library of Science 2012-02-21 /pmc/articles/PMC3283608/ /pubmed/22363503 http://dx.doi.org/10.1371/journal.pone.0030869 Text en Xiao 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 Xiao, Xuan Wang, Pu Chou, Kuo-Chen iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix |
title | iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix |
title_full | iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix |
title_fullStr | iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix |
title_full_unstemmed | iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix |
title_short | iNR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix |
title_sort | inr-physchem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3283608/ https://www.ncbi.nlm.nih.gov/pubmed/22363503 http://dx.doi.org/10.1371/journal.pone.0030869 |
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