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Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model
The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using severa...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3026835/ https://www.ncbi.nlm.nih.gov/pubmed/21283550 http://dx.doi.org/10.1371/journal.pone.0016474 |
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author | Tuzmen, Ceren Erman, Burak |
author_facet | Tuzmen, Ceren Erman, Burak |
author_sort | Tuzmen, Ceren |
collection | PubMed |
description | The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using several benchmark systems where the topology of the unbound protein and the bound protein-ligand complex are known. Predictions are made on the unbound protein. Agreement of results with the bound complexes indicates that the information for binding resides in the unbound protein. Cliques that consist of three or more residues that are far apart along the primary structure but are in contact in the folded structure are shown to be important determinants of the binding problem. Comparison with known structures shows that the predictive capability of the method is significant. |
format | Text |
id | pubmed-3026835 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-30268352011-01-31 Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model Tuzmen, Ceren Erman, Burak PLoS One Research Article The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using several benchmark systems where the topology of the unbound protein and the bound protein-ligand complex are known. Predictions are made on the unbound protein. Agreement of results with the bound complexes indicates that the information for binding resides in the unbound protein. Cliques that consist of three or more residues that are far apart along the primary structure but are in contact in the folded structure are shown to be important determinants of the binding problem. Comparison with known structures shows that the predictive capability of the method is significant. Public Library of Science 2011-01-25 /pmc/articles/PMC3026835/ /pubmed/21283550 http://dx.doi.org/10.1371/journal.pone.0016474 Text en Tuzmen, Erman. 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 Tuzmen, Ceren Erman, Burak Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model |
title | Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model |
title_full | Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model |
title_fullStr | Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model |
title_full_unstemmed | Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model |
title_short | Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model |
title_sort | identification of ligand binding sites of proteins using the gaussian network model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3026835/ https://www.ncbi.nlm.nih.gov/pubmed/21283550 http://dx.doi.org/10.1371/journal.pone.0016474 |
work_keys_str_mv | AT tuzmenceren identificationofligandbindingsitesofproteinsusingthegaussiannetworkmodel AT ermanburak identificationofligandbindingsitesofproteinsusingthegaussiannetworkmodel |