Cargando…
Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method
[Image: see text] Accurate prediction of protein–ligand binding free energies is important in enzyme engineering and drug discovery. The molecular mechanics/generalized Born surface area (MM/GBSA) approach is widely used to estimate ligand-binding affinities, but its performance heavily relies on th...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2021
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655939/ https://www.ncbi.nlm.nih.gov/pubmed/34901645 http://dx.doi.org/10.1021/acsomega.1c04996 |
_version_ | 1784612178625560576 |
---|---|
author | Dong, Lina Qu, Xiaoyang Zhao, Yuan Wang, Binju |
author_facet | Dong, Lina Qu, Xiaoyang Zhao, Yuan Wang, Binju |
author_sort | Dong, Lina |
collection | PubMed |
description | [Image: see text] Accurate prediction of protein–ligand binding free energies is important in enzyme engineering and drug discovery. The molecular mechanics/generalized Born surface area (MM/GBSA) approach is widely used to estimate ligand-binding affinities, but its performance heavily relies on the accuracy of its energy components. A hybrid strategy combining MM/GBSA and machine learning (ML) has been developed to predict the binding free energies of protein–ligand systems. Based on the MM/GBSA energy terms and several features associated with protein–ligand interactions, our ML-based scoring function, GXLE, shows much better performance than MM/GBSA without entropy. In particular, the good transferability of the GXLE model is highlighted by its good performance in ranking power for prediction of the binding affinity of different ligands for either the docked structures or crystal structures. The GXLE scoring function and its code are freely available and can be used to correct the binding free energies computed by MM/GBSA. |
format | Online Article Text |
id | pubmed-8655939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-86559392021-12-10 Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method Dong, Lina Qu, Xiaoyang Zhao, Yuan Wang, Binju ACS Omega [Image: see text] Accurate prediction of protein–ligand binding free energies is important in enzyme engineering and drug discovery. The molecular mechanics/generalized Born surface area (MM/GBSA) approach is widely used to estimate ligand-binding affinities, but its performance heavily relies on the accuracy of its energy components. A hybrid strategy combining MM/GBSA and machine learning (ML) has been developed to predict the binding free energies of protein–ligand systems. Based on the MM/GBSA energy terms and several features associated with protein–ligand interactions, our ML-based scoring function, GXLE, shows much better performance than MM/GBSA without entropy. In particular, the good transferability of the GXLE model is highlighted by its good performance in ranking power for prediction of the binding affinity of different ligands for either the docked structures or crystal structures. The GXLE scoring function and its code are freely available and can be used to correct the binding free energies computed by MM/GBSA. American Chemical Society 2021-11-21 /pmc/articles/PMC8655939/ /pubmed/34901645 http://dx.doi.org/10.1021/acsomega.1c04996 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Dong, Lina Qu, Xiaoyang Zhao, Yuan Wang, Binju Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method |
title | Prediction of Binding Free Energy of Protein–Ligand
Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface
Area and Machine Learning Method |
title_full | Prediction of Binding Free Energy of Protein–Ligand
Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface
Area and Machine Learning Method |
title_fullStr | Prediction of Binding Free Energy of Protein–Ligand
Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface
Area and Machine Learning Method |
title_full_unstemmed | Prediction of Binding Free Energy of Protein–Ligand
Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface
Area and Machine Learning Method |
title_short | Prediction of Binding Free Energy of Protein–Ligand
Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface
Area and Machine Learning Method |
title_sort | prediction of binding free energy of protein–ligand
complexes with a hybrid molecular mechanics/generalized born surface
area and machine learning method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655939/ https://www.ncbi.nlm.nih.gov/pubmed/34901645 http://dx.doi.org/10.1021/acsomega.1c04996 |
work_keys_str_mv | AT donglina predictionofbindingfreeenergyofproteinligandcomplexeswithahybridmolecularmechanicsgeneralizedbornsurfaceareaandmachinelearningmethod AT quxiaoyang predictionofbindingfreeenergyofproteinligandcomplexeswithahybridmolecularmechanicsgeneralizedbornsurfaceareaandmachinelearningmethod AT zhaoyuan predictionofbindingfreeenergyofproteinligandcomplexeswithahybridmolecularmechanicsgeneralizedbornsurfaceareaandmachinelearningmethod AT wangbinju predictionofbindingfreeenergyofproteinligandcomplexeswithahybridmolecularmechanicsgeneralizedbornsurfaceareaandmachinelearningmethod |