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Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters
We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues (N(cons)) and their spatial clustering (ρ(e)), hydrogen bond donor capability (D(p)) and residue propen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3424558/ https://www.ncbi.nlm.nih.gov/pubmed/22641851 http://dx.doi.org/10.1093/nar/gks405 |
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author | Dey, Sucharita Pal, Arumay Guharoy, Mainak Sonavane, Shrihari Chakrabarti, Pinak |
author_facet | Dey, Sucharita Pal, Arumay Guharoy, Mainak Sonavane, Shrihari Chakrabarti, Pinak |
author_sort | Dey, Sucharita |
collection | PubMed |
description | We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues (N(cons)) and their spatial clustering (ρ(e)), hydrogen bond donor capability (D(p)) and residue propensity (R(p)). We first used these parameters to characterize 130 interfaces in a set of 126 DNA-binding proteins (DBPs). The applicability of these parameters both individually and in combination, to distinguish the true binding region from the rest of the protein surface was then analyzed. R(p) shows the best performance identifying the true interface with the top rank in 83% cases. Importantly, we also used the unbound-bound test cases of the protein–DNA docking benchmark to test the efficacy of our method. When applied to the unbound form of the DBPs, R(p) can distinguish 86% cases. Finally, we have applied the SVM approach for recognizing the interface region using the above parameters along with the individual amino acid composition as attributes. The accuracy of prediction is 90.5% for the bound structures and 93.6% for the unbound form of the proteins. |
format | Online Article Text |
id | pubmed-3424558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-34245582012-08-22 Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters Dey, Sucharita Pal, Arumay Guharoy, Mainak Sonavane, Shrihari Chakrabarti, Pinak Nucleic Acids Res Computational Biology We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues (N(cons)) and their spatial clustering (ρ(e)), hydrogen bond donor capability (D(p)) and residue propensity (R(p)). We first used these parameters to characterize 130 interfaces in a set of 126 DNA-binding proteins (DBPs). The applicability of these parameters both individually and in combination, to distinguish the true binding region from the rest of the protein surface was then analyzed. R(p) shows the best performance identifying the true interface with the top rank in 83% cases. Importantly, we also used the unbound-bound test cases of the protein–DNA docking benchmark to test the efficacy of our method. When applied to the unbound form of the DBPs, R(p) can distinguish 86% cases. Finally, we have applied the SVM approach for recognizing the interface region using the above parameters along with the individual amino acid composition as attributes. The accuracy of prediction is 90.5% for the bound structures and 93.6% for the unbound form of the proteins. Oxford University Press 2012-08 2012-05-25 /pmc/articles/PMC3424558/ /pubmed/22641851 http://dx.doi.org/10.1093/nar/gks405 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Dey, Sucharita Pal, Arumay Guharoy, Mainak Sonavane, Shrihari Chakrabarti, Pinak Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters |
title | Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters |
title_full | Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters |
title_fullStr | Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters |
title_full_unstemmed | Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters |
title_short | Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters |
title_sort | characterization and prediction of the binding site in dna-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3424558/ https://www.ncbi.nlm.nih.gov/pubmed/22641851 http://dx.doi.org/10.1093/nar/gks405 |
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