Comparative assessment of QM-based and MM-based models for prediction of protein-ligand binding affinity trends

Iterative Knowledge-Based Scoring Function for Protein-Ligand Interactions by Considering Binding Affinity Information

Scoring functions for protein-ligand interactions play a critical role in structure-based drug design. Owing to the good balance between general applicability and computational efficiency, knowledge-based scoring functions have obtained significant advancements and achieved many successes. Nevertheless, knowledge-based scoring functions face a challenge in utilizing the experimental affinity data and thus may not perform well in binding affinity prediction. Addressing the challenge, we have proposed an improved version of the iterative knowledge-based scoring function ITScore by considering binding affinity information, which is referred to as ITScoreAff, based on a large training set of 6216 protein-ligand complexes with both structures and affinity data. ITScoreAff was extensively evaluated and compared with ITScore, 33 traditional, and 6 machine learning scoring functions in terms of docking power, ranking power, and screening power on the independent CASF-2016 benchmark. It was shown that ITScoreAff obtained an overall better performance than the other 40 scoring functions and gave an average success rate of 85.3% in docking power, a correlation coefficient of 0.723 in scoring power, and an average rank correlation coefficient of 0.668 in ranking power. In addition, ITScoreAff also achieved the overall best screening power when the top 10% of the ranked database were considered. These results demonstrated the robustness of ITScoreAff and its improvement over existing scoring functions.

Exploring Routes to Enhance the Calculation of Free Energy Differences via Non-Equilibrium Work SQM/MM Switching Simulations Using Hybrid Charge Intermediates between MM and SQM Levels of Theory or Non-Linear Switching Schemes

Non-equilibrium work switching simulations and Jarzynski's equation are a reliable method for computing free energy differences, ΔAlow→high, between two levels of theory, such as a pure molecular mechanical (MM) and a quantum mechanical/molecular mechanical (QM/MM) description of a system of interest. Despite the inherent parallelism, the computational cost of this approach can quickly become very high. This is particularly true for systems where the core region, the part of the system to be described at different levels of theory, is embedded in an environment such as explicit solvent water. We find that even for relatively simple solute-water systems, switching lengths of at least 5 ps are necessary to compute ΔAlow→high reliably. In this study, we investigate two approaches towards an affordable protocol, with an emphasis on keeping the switching length well below 5 ps. Inserting a hybrid charge intermediate state with modified partial charges, which resembles the charge distribution of the desired high level, makes it possible to obtain reliable calculations with 2 ps switches. Attempts using step-wise linear switching paths, on the other hand, did not lead to improvement, i.e., a faster convergence for all systems. To understand these findings, we analyzed the solutes' properties as a function of the partial charges used and the number of water molecules in direct contact with the solute, and studied the time needed for water molecules to reorient themselves upon a change in the solute's charge distribution.

Integration of In Silico Strategies for Drug Repositioning towards P38α Mitogen-Activated Protein Kinase (MAPK) at the Allosteric Site

P38α mitogen-activated protein kinase (p38α MAPK), one of the p38 MAPK isoforms participating in a signaling cascade, has been identified for its pivotal role in the regulation of physiological processes such as cell proliferation, differentiation, survival, and death. Herein, by shedding light on docking- and 100-ns dynamic-based screening from 3210 FDA-approved drugs, we found that lomitapide (a lipid-lowering agent) and nilotinib (a Bcr-Abl fusion protein inhibitor) could alternatively inhibit phosphorylation of p38α MAPK at the allosteric site. All-atom molecular dynamics simulations and free energy calculations including end-point and QM-based ONIOM methods revealed that the binding affinity of the two screened drugs exhibited a comparable level as the known p38α MAPK inhibitor (BIRB796), suggesting the high potential of being a novel p38α MAPK inhibitor. In addition, noncovalent contacts and the number of hydrogen bonds were found to be corresponding with the great binding recognition. Key influential amino acids were mostly hydrophobic residues, while the two charged residues including E71 and D168 were considered crucial ones due to their ability to form very strong H-bonds with the focused drugs. Altogether, our contributions obtained here could be theoretical guidance for further conducting experimental-based preclinical studies necessary for developing therapeutic agents targeting p38α MAPK.

BRD4: quantum mechanical protein–ligand binding free energies using the full-protein DFT-based QM-PBSA method

Fully quantum mechanical approaches to calculating protein–ligand free energies of binding have the potential to reduce empiricism and explicitly account for all physical interactions responsible for protein–ligand binding.