Hybrid QM/MM Free-Energy Evaluation of Drug-Resistant Mutational Effect on the Binding of an Inhibitor Indinavir to HIV-1 Protease

Algorithm for Efficient Superposition and Clustering of Molecular Assemblies Using the Branch-and-Bound Method

The root-mean-square deviation (RMSD) is one of the most common metrics for comparing the similarity of three-dimensional chemical structures. The chemical structure similarity plays an important role in data chemistry because it is closely related to chemical reactivity, physical properties, and bioactivity. Despite the wide applicability of the RMSD, the simultaneous determination of atom mapping and spatial superposition of RMSD remains a challenging problem to solve in polynomial time. We introduce an algorithm called mobbRMSD, which is formulated in molecular-oriented coordinates and uses the branch-and-bound method to obtain an exact solution for the RMSD. mobbRMSD can efficiently handle a wide range of chemical systems, such as molecular liquids, solute solvations, and self-assembly of large molecules, using chemical knowledge such as atom types, chemical bonding, and chirality. In benchmarks involving small molecular aggregates, mobbRMSD extends the limiting system size of existing exact solution methods by almost twice. Furthermore, mobbRMSD demonstrated the ability to analyze the structural similarity of large molecular micelles, which has been difficult with previous methods. We also propose a mobbRMSD-based structural clustering method designed for molecular dynamics trajectories, which improves the computational cost of branch-and-bound methods to asymptotically average the polynomial time as the number of data increases. Our algorithm is freely available at https://github.com/yymmt742/mobbrmsd.

Bacteriorhodopsin proton-pumping mechanism: Successes and challenges in computational approaches

Bacteriorhodopsin (bR) is perhaps the best-studied proton pump. Over about four decades, research on this fascinating photocyclic light-driven protein inspired the development of key experimental and computational methodologies that are now widely used in membrane protein studies. We review here failures and successes in computational approaches that have been applied to study the bR proton-transfer steps. Conflict between experimental results pertaining to the proton transfer mechanisms in the early photocycle intermediates was resolved by detailed quantum mechanical/molecular mechanical computation, the results of which were confirmed more than a decade later. Key to this approach was the realization that, to understand how the pump works and achieves directional transfer of protons, the individual reaction steps-proton transfer and reorganization of the internal hydrogen-bond network-needed to be considered within the context of the energy landscape of the complete reaction cycle.

How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms

Molecular simulations are an essential asset in the first steps of drug design campaigns. However, the requirement of high-throughput limits applications mainly to qualitative approaches with low computational cost, but also low accuracy. Unlocking the potential of more rigorous quantum mechanical/molecular mechanics (QM/MM) models combined with molecular dynamics-based free energy techniques could have a tremendous impact. Indeed, these two relatively old techniques are emerging as promising methods in the field. This has been favored by the exponential growth of computer power and the proliferation of powerful data-driven methods. Here, we briefly review recent advances and applications, and give our perspective on the impact that QM/MM and free-energy methods combined with machine learning-aided algorithms can have on drug design.

Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations

The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.

Mesoscale simulations: An indispensable approach to understand biomembranes

Computer simulation techniques form a versatile tool, a computational microscope, for exploring biological processes. This tool has been particularly effective in exploring different features of biological membranes. In recent years, thanks to elegant multiscale simulation schemes, some fundamental limitations of investigations by distinct simulation techniques have been resolved. As a result, we are now capable of exploring processes spanning multiple scales beyond the capacity of any single technique. In this perspective, we argue that mesoscale simulations require more attention and must be further developed to fill evident gaps in a quest toward simulating and modeling living cell membranes.

Benefits of hybrid QM/MM over traditional classical mechanics in pharmaceutical systems

Hybrid quantum mechanics/molecular mechanics (QM/MM) is one of the most reliable approaches for accurately modeling and studying the complex pharmaceutical discovery system. Classical mechanics has significantly accelerated the drug discovery process in the past decade. However, the current challenge is the large pool of false positives, which require extensive validation. Hybrid QM/MM is an effective solution for accurately studying ligand binding, structural mechanisms, free energy evaluation, and spectroscopic characterization. This article highlights the methodological details relevant to cost-effective hybrid QM/MM methods. This approach, combined with traditional pharmacoinformatics methods, could be a reliable strategy to balance the cost and accuracy of the calculations.

Molecular docking used as an advanced tool to determine novel compounds on emerging infectious diseases: A systematic review

Emerging infectious diseases (EID) as well as reappearing irresistible infections are expanding worldwide. Utmost of similar cases, it was seen that the EIDs have long been perceived as a predominant conclusion of host-pathogen adaption. Here, one should get to analyze their host-pathogen interlink and their by needs to look ways, as an example, by exploitation process methodology particularly molecular docking and molecular dynamics simulation, have been utilized in recent time as the most outstanding tools. Hence, we have overviewed some of important factors that influences on EIDs especially HIV/AIDs, H1N1 and coronavirus. Moreover, here we specified the importance of molecular docking applications especially molecular dynamics simulations approach to determine novel compounds on the emerging infectious diseases. Additionally, in vivo and in vitro studies approach to determine novel compounds on the emerging infectious diseases that has implemented to evaluate the limiting affinities between small particles as well as macromolecule that can further, used as a target of HIV/AIDs, H1N1, and coronavirus were also discussed. These novel drug molecules approved in vivo and in vitro studies with reaffirm results and hence, it is clear that the computational methods (mainly molecular docking and molecular dynamics) are found to be more effective technique for drug discovery and medical practitioners.