Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set.

Sun, Huiyong; Li, Youyong; Tian, Sheng; Xu, Lei; Hou, Tingjun

doi:10.1039/c4cp01388c

Cited by 738Computational Drug Discovery MethodsProtein Structure and DynamicsAdvanced Chemical Physics Studies

Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set.

Sun, Huiyong, Li, Youyong, Tian, Sheng, Xu, Lei, Hou, Tingjun·2014·Phys. Chem. Chem. Phys.

DOI 10.1039/c4cp01388c

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Abstract

By using different evaluation strategies, we systemically evaluated the performance of Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodologies based on more than 1800 protein-ligand crystal structures in the PDBbind database. The results can be summarized as follows: (1) for the one-protein-family/one-binding-ligand case which represents the unbiased protein-ligand complex sampling, both MM/GBSA and MM/PBSA methodologies achieve approximately equal accuracies at the interior dielectric constant of 4 (with rp = 0.408 ± 0.006 of MM/GBSA and rp = 0.388 ± 0.006 of MM/PBSA based on the minimized structures); while for the total dataset (1864 crystal structures), the overall best Pearson correlation coefficient (rp = 0.579 ± 0.002) based on MM/GBSA is better than that of MM/PBSA (rp = 0.491 ± 0.003), indicating that biased sampling may significantly affect the accuracy of the predicted result (some protein families contain too many instances and can bias the overall predicted accuracy). Therefore, family based classification is needed to evaluate the two methodologies; (2) the prediction accuracies of MM/GBSA and MM/PBSA for different protein families are quite different with rp ranging from 0 to 0.9, whereas the correlation and ranking scores (an averaged rp/rs over a list of protein folds and also representing the unbiased sampling) given by MM/PBSA (rp-score = 0.506 ± 0.050 and rs-score = 0.481 ± 0.052) are comparable to those given by MM/GBSA (rp-score = 0.516 ± 0.047 and rs-score = 0.463 ± 0.047) at the fold family level; (3) for the overall prediction accuracies, molecular dynamics (MD) simulation may not be quite necessary for MM/GBSA (rp-minimized = 0.579 ± 0.002 and rp-1ns = 0.564 ± 0.002), but is needed for MM/PBSA (rp-minimized = 0.412 ± 0.003 and rp-1ns = 0.491 ± 0.003). However, for the individual systems, whether to use MD simulation is depended. (4) both MM/GBSA and MM/PBSA may be unable to give successful predictions for the ligands with high formal charges, with the Pearson correlation coefficient ranging from 0.621 ± 0.003 (neutral ligands) to 0.125 ± 0.142 (ligands with a formal charge of 5). Therefore, it can be summarized that, although MM/GBSA and MM/PBSA perform similarly in the unbiased dataset, for the currently available crystal structures in the PDBbind database, compared with MM/GBSA, which may be used in multi-target comparisons, MM/PBSA is more sensitive to the investigated systems, and may be more suitable for individual-target-level binding free energy ranking. This study may provide useful guidance for the post-processing of docking based studies.

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MM-GBSA is less sensitive to the investigated systems and is more suitable to be used in general cases than MM-PBSA.
“They concluded that although similar results were observed, MM-GBSA is less sensitive to the investigated systems and is more suitable to be used in general cases (e.g., reverse docking, which is widely used to predict the receptor target(s) of a compound).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Sun et al., 2014

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Authors: Sun, H., Li, Y., Tian, S., Xu, L., & Hou, T.
Affiliations: Soochow University (CN)
Zhejiang University (CN)
Corresponding author: Hou, Tingjun
ORCID: Sun, Huiyong
Li, Youyong
Tian, Sheng
Xu, Lei
Hou, Tingjun
Venue: Phys. Chem. Chem. Phys.
Publisher: Royal Society of Chemistry (RSC)
Published: 2014
Volume: 16
Issue: 31
Pages: 16719-16729
ISSN: 1463-9076 (ISSN-L)
1463-9084
DOI: 10.1039/c4cp01388c
OpenAlex: W2025116694
Cited by: 738
References: 73
Citations by year: 2026: 47 · 2025: 48 · 2024: 78 · 2023: 85 · 2022: 73 · 2021: 72 · 2020: 78 · 2019: 68 · 2018: 67 · 2017: 49 · 2016: 38 · 2015: 30 · 2014: 4
FWCI: 42.00
Type: journal article
Language: en
Open access: closed
Locations: Physical Chemistry Chemical Physics (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords: MM-PBSA, MM-GBSA, binding free energy prediction, protein-ligand binding affinity, PDBbind, dielectric constant, molecular dynamics simulation, binding affinity ranking, Poisson-Boltzmann equation, generalized Born, binding site flexibility
MeSH terms: Ligands, Molecular Conformation, Reproducibility of Results, Databases, Protein, Molecular Dynamics Simulation

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Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set.

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