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Bringing Clarity to the Prediction of Protein–Ligand Binding Free Energies via “Blurring”
[Image: see text] We present a method to evaluate the free energies of ligand binding utilizing a Monte Carlo estimation of the configuration integrals concomitant with uncertainty quantification. Ensembles for integration are built through systematically perturbing an initial ligand conformation in...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4006398/ https://www.ncbi.nlm.nih.gov/pubmed/24803861 http://dx.doi.org/10.1021/ct400995c |
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author | Ucisik, Melek N. Zheng, Zheng Faver, John C. Merz, Kenneth M. |
author_facet | Ucisik, Melek N. Zheng, Zheng Faver, John C. Merz, Kenneth M. |
author_sort | Ucisik, Melek N. |
collection | PubMed |
description | [Image: see text] We present a method to evaluate the free energies of ligand binding utilizing a Monte Carlo estimation of the configuration integrals concomitant with uncertainty quantification. Ensembles for integration are built through systematically perturbing an initial ligand conformation in a rigid binding pocket, which is optimized separately prior to incorporation of the ligand. We call the procedure producing the ensembles “blurring”, and it is carried out using an in-house developed code. The Boltzmann factor contribution of each pose to the configuration integral is computed and from there the free energy is obtained. Potential function uncertainties are estimated using a fragment-based error propagation method. This method has been applied to a set of small aromatic ligands complexed with T4 Lysozyme L99A mutant. Microstate energies have been determined with the force fields ff99SB and ff94, and the semiempirical method PM6DH2 in conjunction with continuum solvation models including Generalized Born (GB), the Conductor-like Screening Model (COSMO), and SMD. Of the methods studied, PM6DH2-based scoring gave binding free energy estimates, which yielded a good correlation to the experimental binding affinities (R(2) = 0.7). All methods overestimated the calculated binding affinities. We trace this to insufficient sampling, the single static protein structure, and inaccuracies in the solvent models we have used in this study. |
format | Online Article Text |
id | pubmed-4006398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-40063982015-02-07 Bringing Clarity to the Prediction of Protein–Ligand Binding Free Energies via “Blurring” Ucisik, Melek N. Zheng, Zheng Faver, John C. Merz, Kenneth M. J Chem Theory Comput [Image: see text] We present a method to evaluate the free energies of ligand binding utilizing a Monte Carlo estimation of the configuration integrals concomitant with uncertainty quantification. Ensembles for integration are built through systematically perturbing an initial ligand conformation in a rigid binding pocket, which is optimized separately prior to incorporation of the ligand. We call the procedure producing the ensembles “blurring”, and it is carried out using an in-house developed code. The Boltzmann factor contribution of each pose to the configuration integral is computed and from there the free energy is obtained. Potential function uncertainties are estimated using a fragment-based error propagation method. This method has been applied to a set of small aromatic ligands complexed with T4 Lysozyme L99A mutant. Microstate energies have been determined with the force fields ff99SB and ff94, and the semiempirical method PM6DH2 in conjunction with continuum solvation models including Generalized Born (GB), the Conductor-like Screening Model (COSMO), and SMD. Of the methods studied, PM6DH2-based scoring gave binding free energy estimates, which yielded a good correlation to the experimental binding affinities (R(2) = 0.7). All methods overestimated the calculated binding affinities. We trace this to insufficient sampling, the single static protein structure, and inaccuracies in the solvent models we have used in this study. American Chemical Society 2014-02-07 2014-03-11 /pmc/articles/PMC4006398/ /pubmed/24803861 http://dx.doi.org/10.1021/ct400995c Text en Copyright © 2014 American Chemical Society |
spellingShingle | Ucisik, Melek N. Zheng, Zheng Faver, John C. Merz, Kenneth M. Bringing Clarity to the Prediction of Protein–Ligand Binding Free Energies via “Blurring” |
title | Bringing Clarity to the Prediction of Protein–Ligand
Binding Free Energies via “Blurring” |
title_full | Bringing Clarity to the Prediction of Protein–Ligand
Binding Free Energies via “Blurring” |
title_fullStr | Bringing Clarity to the Prediction of Protein–Ligand
Binding Free Energies via “Blurring” |
title_full_unstemmed | Bringing Clarity to the Prediction of Protein–Ligand
Binding Free Energies via “Blurring” |
title_short | Bringing Clarity to the Prediction of Protein–Ligand
Binding Free Energies via “Blurring” |
title_sort | bringing clarity to the prediction of protein–ligand
binding free energies via “blurring” |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4006398/ https://www.ncbi.nlm.nih.gov/pubmed/24803861 http://dx.doi.org/10.1021/ct400995c |
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