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Prediction of kinase-inhibitor binding affinity using energetic parameters

The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log...

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Detalles Bibliográficos
Autores principales: Usha, Singaravelu, Selvaraj, Samuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5267961/
https://www.ncbi.nlm.nih.gov/pubmed/28149052
http://dx.doi.org/10.6026/97320630012172
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author Usha, Singaravelu
Selvaraj, Samuel
author_facet Usha, Singaravelu
Selvaraj, Samuel
author_sort Usha, Singaravelu
collection PubMed
description The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log (IC50) values of 25 different kinase-inhibitors using multiple regressions which gave a correlation coefficient of 0.93. The regression equation obtained was tested on 93 kinase-inhibitor complexes and an average deviation of 0.92 from the experimental log IC50 values was shown. The same set of descriptors was used to predict binding affinities for a test set of five individual kinase families, with correlation values > 0.9. We show that the protein-ligand interaction energies and partition coefficient values form the major deterministic factors for binding affinity of the ligand for its receptor.
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spelling pubmed-52679612017-02-01 Prediction of kinase-inhibitor binding affinity using energetic parameters Usha, Singaravelu Selvaraj, Samuel Bioinformation Hypothesis The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log (IC50) values of 25 different kinase-inhibitors using multiple regressions which gave a correlation coefficient of 0.93. The regression equation obtained was tested on 93 kinase-inhibitor complexes and an average deviation of 0.92 from the experimental log IC50 values was shown. The same set of descriptors was used to predict binding affinities for a test set of five individual kinase families, with correlation values > 0.9. We show that the protein-ligand interaction energies and partition coefficient values form the major deterministic factors for binding affinity of the ligand for its receptor. Biomedical Informatics 2016-06-15 /pmc/articles/PMC5267961/ /pubmed/28149052 http://dx.doi.org/10.6026/97320630012172 Text en © 2016 Biomedical Informatics This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
spellingShingle Hypothesis
Usha, Singaravelu
Selvaraj, Samuel
Prediction of kinase-inhibitor binding affinity using energetic parameters
title Prediction of kinase-inhibitor binding affinity using energetic parameters
title_full Prediction of kinase-inhibitor binding affinity using energetic parameters
title_fullStr Prediction of kinase-inhibitor binding affinity using energetic parameters
title_full_unstemmed Prediction of kinase-inhibitor binding affinity using energetic parameters
title_short Prediction of kinase-inhibitor binding affinity using energetic parameters
title_sort prediction of kinase-inhibitor binding affinity using energetic parameters
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5267961/
https://www.ncbi.nlm.nih.gov/pubmed/28149052
http://dx.doi.org/10.6026/97320630012172
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