Molecular mechanics Poisson−Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) are arguably very popular methods for binding free energy prediction since they are more accurate than most scoring functions of molecular docking and less computationally demanding than alchemical free energy methods. MM/PBSA and MM/GBSA have been widely used in biomolecular studies such as protein folding, protein−ligand binding, protein−protein interaction, etc. In this review, methods to adjust the polar solvation energy and to improve the performance of MM/PBSA and MM/GBSA calculations are reviewed and discussed. The latest applications of MM/GBSA and MM/PBSA in drug design are also presented. This review intends to provide readers with guidance for practically applying MM/PBSA and MM/GBSA in drug design and related research fields. CONTENTS 1. Introduction 9478 2. Methodology 9480 3. Assessing the Performance of MM/PBSA and MM/GBSA 9484 4. The Polar Solvation Energy and Entropy Terms in MM/PB(GB)SA Calculations 9485 4.1. The Polar Solvation Energy Term in MM/ PBSA 9485 4.2. The Polar Energy Solvation Term in MM/ GBSA 9486 4.3. Theory, Implementation, and Limitations of the Variable Dielectric Model in MM/GBSA 9487 4.4. Comparison between PB and GB 9488 4.5. Efficient Entropy Calculation Methods To Estimate the Entropy Change upon Ligand Binding 9488 5.

In structure-based drug design (SBDD), the molecular mechanics generalized Born surface area (MM/GBSA) approach has been widely used in ranking the binding affinity of small molecule ligands. However, an accurate estimation of protein–ligand binding affinity still remains a challenge due to the intrinsic limitation of the standard generalized Born (GB) model used in MM/GBSA. In this study, we proposed and evaluated the MM/GBSA approach based on a variable dielectric generalized Born (VDGB) model using residue-type-based dielectric constants. In the VDGB model, different dielectric values were assigned for the three types of protein residues, and the magnitude of the dielectric constants for residue types follows this order: charged ≥ polar ≥ nonpolar. We found that MM/GBSA based on a VDGB model (MM/GBSAVDGB) with an optimal dielectric constant of 4.0 for the charged residues and 1.0 for the noncharged residues together with a net-charge-dependent dielectric value for ligands achieved better predictions as judged by Pearson’s correlation coefficient than the standard MM/GBSA with a uniform solute dielectric constant of 4.0 for the training set of 130 protein–ligand complexes. The prediction on the test set with 165 protein–ligand complexes also validated the better performance of MM/GBSAVDGB. Moreover, this method exhibited potential in predicting the relative binding free energies for multiple ligands against the same target. Furthermore, we found that rational truncation of protein residues far from the binding site can significantly speed up the MM/GBSAVDGB calculations, while it almost does not influence the prediction accuracy. Therefore, it is feasible to implement the system-truncated MM/GBSAVDGB as a scoring function for SBDD.

The molecular mechanics/generalized Born surface area (MM/GBSA) has been widely used in end-point binding free energy prediction in structure-based drug design (SBDD). However, in practice, it is usually being treated as a disputed method mostly because of its system dependence. Here, combining with machine-learning optimization, we developed a novel version of MM/GBSA, named variable atomic dielectric MM/GBSA (VAD-MM/GBSA), by assigning variable dielectric constants directly to the protein/ligand atoms. The new strategy exhibits markedly improved accuracy in binding affinity calculations for various protein–ligand systems and is promising to be used in the postprocessing of structure-based virtual screening. Moreover, VAD-MM/GBSA outperformed prime MM/GBSA in Schrödinger software and showed remarkable predictive performance for specific protein targets, such as POL polyprotein, human immunodeficiency virus type 1 (HIV-1) protease, etc. Our study showed that the VAD-MM/GBSA method with little extra computational overhead provides a potential replacement of the MM/GBSA in AMBER software. An online web server of VAD-MMGBSA has been developed and is now available at .

The immune checkpoint pathway of human programmed cell death 1 (hPD-1) and human programmed cell death ligand 1 (hPD-L1) is a promising target for cancer treatment. The blockade of the interplay between hPD-1 and hPD-L1 has recently shown good therapeutic efficacy. Although crystallographic studies have provided static conformational snapshots of the interface between hPD-1 and hPD-L1, the hot spot residues on both proteins that play key roles in the association process still remain elusive. To this end, we performed a series of alchemical free-energy simulations to analyze the energetic contributions of the interfacial residues on both hPD-1 and hPD-L1 and investigated the distributional patterns of the residues that significantly contribute to the binding. The results suggest that the hot spots on hPD-1 comprise Tyr68, Gln75, Ile126, Leu128, Ile134, and Glu136, and the hot spots on hPD-L1 comprise LAsp26 (the L symbol refers to hPD-L1), LIle54, LTyr56, LMet115, LAsp122, LTyr123, and LLys124. Moreover, we found that the distribution of these hot spot residues is highly uneven with respect to either the energetic contribution or the side-chain polarity, with energetically important residues clustered within densely packed hydrophobic regions. The mechanism ruling the interaction of the two binding partners is also discussed in detail from the perspective of the O-ring theory. Our work provides clues for the future development of anticancer inhibitors targeting the hPD-1/hPD-L1 immune checkpoint pathway.