GPU Accelerated Quantum Virtual Screening: Application for the Natural Inhibitors of New Dehli Metalloprotein (NDM-1)

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.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches

Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.

Enzyme Inhibitors: The Best Strategy to Tackle Superbug NDM-1 and Its Variants

Multidrug bacterial resistance endangers clinically effective antimicrobial therapy and continues to cause major public health problems, which have been upgraded to unprecedented levels in recent years, worldwide. β-Lactam antibiotics have become an important weapon to fight against pathogen infections due to their broad spectrum. Unfortunately, the emergence of antibiotic resistance genes (ARGs) has severely astricted the application of β-lactam antibiotics. Of these, New Delhi metallo-β-lactamase-1 (NDM-1) represents the most disturbing development due to its substrate promiscuity, the appearance of variants, and transferability. Given the clinical correlation of β-lactam antibiotics and NDM-1-mediated resistance, the discovery, and development of combination drugs, including NDM-1 inhibitors, for NDM-1 bacterial infections, seems particularly attractive and urgent. This review summarizes the research related to the development and optimization of effective NDM-1 inhibitors. The detailed generalization of crystal structure, enzyme activity center and catalytic mechanism, variants and global distribution, mechanism of action of existing inhibitors, and the development of scaffolds provides a reference for finding potential clinically effective NDM-1 inhibitors against drug-resistant bacteria.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Molecular dynamics investigations of oligosaccharides recognized by family 16 and 22 carbohydrate binding modules

As a non-catalytic domain, carbohydrate binding modules (CBMs) are often considered to play some key roles in the degradation and recognition of polysaccharides catalyzed by cellulases. In this work, we investigated the recognition dynamics of cello- or xylo-saccharides by two typical CBMs (CBM16-1 and CBM22-2), which are grouped into Type B CBMs. By combining extensive molecular dynamics, principle component analysis, and binding free energy calculations, we constructed several complex models of the two CBMs in both complex cello- and xylo-oligosaccharides. The corresponding substrate recognition affinity and critical residues having significant contributions were systematically investigated. The residues containing aromatic side chain groups were shown to contribute significantly to substrate binding. The calculated binding free energies were in fairly good agreement with the experimental measurements with the absolute mean error of 0.69 kcal mol^-1. The overall electrostatic interactions were shown to have negative effects on substrate recognition. Further metadynamics simulations revealed the substrate dissociation process.

Emerging Screening Approaches in the Development of Nrf2-Keap1 Protein-Protein Interaction Inhibitors

Due to role of the Keap1–Nrf2 protein–protein interaction (PPI) in protecting cells from oxidative stress, the development of small molecule inhibitors that inhibit this interaction has arisen as a viable approach to combat maladies caused by oxidative stress, such as cancers, neurodegenerative disease and diabetes. To obtain specific and genuine Keap1–Nrf2 inhibitors, many efforts have been made towards developing new screening approaches. However, there is no inhibitor for this target entering the clinic for the treatment of human diseases. New strategies to identify novel bioactive compounds from large molecular databases and accelerate the developmental process of the clinical application of Keap1–Nrf2 protein–protein interaction inhibitors are greatly needed. In this review, we have summarized virtual screening and other methods for discovering new lead compounds against the Keap1–Nrf2 protein–protein interaction. We also discuss the advantages and limitations of different strategies, and the potential of this PPI as a drug target in disease therapy.