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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2016
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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. |
format | Online Article Text |
id | pubmed-5267961 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ushasingaravelu predictionofkinaseinhibitorbindingaffinityusingenergeticparameters AT selvarajsamuel predictionofkinaseinhibitorbindingaffinityusingenergeticparameters |