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

• Published in: Physical chemistry chemical physics : PCCP Vol. 16; no. 31; pp. 16719 - 16729
• Main Authors: Sun, Huiyong, Li, Youyong, Tian, Sheng, Xu, Lei, Hou, Tingjun
• Format: Journal Article
• Language: English
• Published: England 21.08.2014
• Subjects: Correlation coefficients; Crystal structure; Databases, Protein; Ligands; Molecular Conformation; Molecular Dynamics Simulation; Proteins; Ranking; Reproducibility of Results; Sampling; Simulation
• ISSN: 1463-9076; 1463-9084; 1463-9084
• DOI: 10.1039/C4CP01388C

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 r p = 0.408 ± 0.006 of MM/GBSA and r p = 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 ( r p = 0.579 ± 0.002) based on MM/GBSA is better than that of MM/PBSA ( r p = 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 r p ranging from 0 to 0.9, whereas the correlation and ranking scores (an averaged r p / r s over a list of protein folds and also representing the unbiased sampling) given by MM/PBSA ( r p -score = 0.506 ± 0.050 and r s -score = 0.481 ± 0.052) are comparable to those given by MM/GBSA ( r p -score = 0.516 ± 0.047 and r s -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 ( r p-minimized = 0.579 ± 0.002 and r p-1ns = 0.564 ± 0.002), but is needed for MM/PBSA ( r p-minimized = 0.412 ± 0.003 and r p-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.