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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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Detalles Bibliográficos
Autores principales: Tuzmen, Ceren, Erman, Burak
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
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.
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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
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