Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking

Abstract

In molecular docking, it is challenging to develop a scoring function that is accurate to conduct high-throughput screenings. Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) in molecular docking studies. Here, we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein-ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric constant 2.0, produced a Spearman's correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free-energy estimations and is efficient to re-score the top-hit poses produced by other less-accurate scoring functions.

Figure 1

The correlations between the predicted binding free energies of MM/GBSA and the RMSD values for three complexes with the best correlations and three complexes with the worst correlations.

Figure 2

The interaction between Rnase T1 Mutant Glu46Gln and (a) conformation 13 and (b) conformation 30 of ligand 2'GMP. The charged phosphate groups are shown in red stick; the Connolly surfaces for Glu50, Arg77 and His92 are colored in yellow, violet and blue, respectively.

Figure 3

The coordinate bonds formed by the zinc ion with one phosphoramidate oxygen atom, one glutamin and two histidine residues. The zinc ion is colored in yellow, protein residues colored in blue, and ligand colored in violet.

Figure 4

The interaction between chloramphenicol and chloramphenicol acetyltransferase in complex with 3cla. Ligand is colored in violet. The water molecules within 5 Å of ligand are shown as the CPK model, and the water molecule which can mediate the interactions between ligand and proteins are colored in red.

Figure 5

The linear correlations coefficients between the experimental binding free energies and the predicted values given by (a) MM/GBSA using the GB^HCT model and the solute dielectric constant of 2 based on the experimentally observed conformations, (b) MM/GBSA using the GB^HCT model and the solute dielectric constant of 2 based on the best scored conformations, (c) MM/GBSA using the GB^OBC1 model and the solute dielectric constant of 2 based on the experimentally observed conformations, (d) MM/GBSA using the GB^OBC1 model and the solute dielectric constant of 2 based on the best scored conformations, (f) MM/PBSA using the solute dielectric constant of 2 based on the experimentally observed conformations, (g) MM/PBSA using the solute dielectric constant of 2 based on the best scored conformations.