Cargando…
An Efficient Computational Method for Calculating Ligand Binding Affinities
Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating pr...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3423425/ https://www.ncbi.nlm.nih.gov/pubmed/22916168 http://dx.doi.org/10.1371/journal.pone.0042846 |
_version_ | 1782241108906999808 |
---|---|
author | Suenaga, Atsushi Okimoto, Noriaki Hirano, Yoshinori Fukui, Kazuhiko |
author_facet | Suenaga, Atsushi Okimoto, Noriaki Hirano, Yoshinori Fukui, Kazuhiko |
author_sort | Suenaga, Atsushi |
collection | PubMed |
description | Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈±1.5 kcal•mol(−1) of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values. |
format | Online Article Text |
id | pubmed-3423425 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-34234252012-08-22 An Efficient Computational Method for Calculating Ligand Binding Affinities Suenaga, Atsushi Okimoto, Noriaki Hirano, Yoshinori Fukui, Kazuhiko PLoS One Research Article Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈±1.5 kcal•mol(−1) of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values. Public Library of Science 2012-08-20 /pmc/articles/PMC3423425/ /pubmed/22916168 http://dx.doi.org/10.1371/journal.pone.0042846 Text en © 2012 Suenaga et al 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 Suenaga, Atsushi Okimoto, Noriaki Hirano, Yoshinori Fukui, Kazuhiko An Efficient Computational Method for Calculating Ligand Binding Affinities |
title | An Efficient Computational Method for Calculating Ligand Binding Affinities |
title_full | An Efficient Computational Method for Calculating Ligand Binding Affinities |
title_fullStr | An Efficient Computational Method for Calculating Ligand Binding Affinities |
title_full_unstemmed | An Efficient Computational Method for Calculating Ligand Binding Affinities |
title_short | An Efficient Computational Method for Calculating Ligand Binding Affinities |
title_sort | efficient computational method for calculating ligand binding affinities |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3423425/ https://www.ncbi.nlm.nih.gov/pubmed/22916168 http://dx.doi.org/10.1371/journal.pone.0042846 |
work_keys_str_mv | AT suenagaatsushi anefficientcomputationalmethodforcalculatingligandbindingaffinities AT okimotonoriaki anefficientcomputationalmethodforcalculatingligandbindingaffinities AT hiranoyoshinori anefficientcomputationalmethodforcalculatingligandbindingaffinities AT fukuikazuhiko anefficientcomputationalmethodforcalculatingligandbindingaffinities AT suenagaatsushi efficientcomputationalmethodforcalculatingligandbindingaffinities AT okimotonoriaki efficientcomputationalmethodforcalculatingligandbindingaffinities AT hiranoyoshinori efficientcomputationalmethodforcalculatingligandbindingaffinities AT fukuikazuhiko efficientcomputationalmethodforcalculatingligandbindingaffinities |