In Silico Prediction of Ligand Binding Energies in Multiple Therapeutic Targets and Diverse Ligand Sets—A Case Study on BACE1, TYK2, HSP90, and PERK Proteins

Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy

We developed an effective binding free energy prediction protocol which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations to substitute the specified atomic charges of force fields with quantum-mechanically recalculated ones at a proposed pose using a mining minima approach with the VeraChem mining minima engine. We tested this protocol using seven well-known targets with 147 different ligands and compared it with classical mining minima and the most popular binding free energy (BFE) methods using different metrics. Our new protocol, dubbed Qcharge-VM2, yielded an overall Pearson correlation of 0.86, which was better than all the methods examined. Qcharge-VM2 performed significantly better than implicit solvent-based methods, such as MM-GBSA and MM-PBSA, but not as good as explicit water-based free energy perturbation methods, such as FEP+, in terms of root-mean-square error, RMSE (1.75 kcal/mol) and mean unsigned error, MUE (1.39 kcal/mol) on a limited set of targets. However, our protocol is substantially less computationally demanding compared with FEP+. The combined accuracy and efficiency of our method can be valuable in drug discovery campaigns.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

Alzheimer's Disease and β-Secretase Inhibition: An Update With a Focus on Computer-Aided Inhibitor Design

INTRODUCTION

Alzheimer's disease (AD) is an intensifying neurodegenerative illness due to its irreversible nature. Identification of β-site amyloid precursor protein (APP) cleaving enzyme1 (BACE1) has been a significant medicinal focus towards AD treatment, and this has opened ground for several investigations. Despite the numerous works in this direction, no BACE1 inhibitor has made it to the final approval stage as an anti-AD drug.

METHOD

We provide an introductory background of the subject with a general overview of the pathogenesis of AD. The review features BACE1 inhibitor design and development with a focus on some clinical trials and discontinued drugs. Using the topical keywords BACE1, inhibitor design, and computational/theoretical study in the Web of Science and Scopus database, we retrieved over 49 relevant articles. The search years are from 2010 and 2020, with analysis conducted from May 2020 to March 2021.

RESULTS AND DISCUSSION

Researchers have employed computational methodologies to unravel potential BACE1 inhibitors with a significant outcome. The most used computer-aided approach in BACE1 inhibitor design and binding/interaction studies are pharmacophore development, quantitative structure-activity relationship (QSAR), virtual screening, docking, and molecular dynamics (MD) simulations. These methods, plus more advanced ones including quantum mechanics/molecular mechanics (QM/MM) and QM, have proven substantial in the computational framework for BACE1 inhibitor design. Computational chemists have embraced the incorporation of in vitro assay to provide insight into the inhibition performance of identified molecules with potential inhibition towards BACE1. Significant IC₅₀ values up to 50 nM, better than clinical trial compounds, are available in the literature.

CONCLUSION

The continuous failure of potent BACE1 inhibitors at clinical trials is attracting many queries prompting researchers to investigate newer concepts necessary for effective inhibitor design. The considered properties for efficient BACE1 inhibitor design seem enormous and require thorough scrutiny. Lately, researchers noticed that besides appreciable binding affinity and blood-brain barrier (BBB) permeation, BACE1 inhibitor must show low or no affinity for permeability-glycoprotein. Computational modeling methods have profound applications in drug discovery strategy. With the volume of recent in silico studies on BACE1 inhibition, the prospect of identifying potent molecules that would reach the approved level is feasible. Investigators should try pushing many of the identified BACE1 compounds with significant anti-AD properties to preclinical and clinical trial stages. We also advise computational research on allosteric inhibitor design, exosite modeling, and multisite inhibition of BACE1. These alternatives might be a solution to BACE1 drug discovery in AD therapy.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer's Therapeutics

Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer's is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer's small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer's. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.