Quantum polarized ligand docking investigation to understand the significance of protonation states in histone deacetylase inhibitors

Probing the bioactive compounds of Kigelia africana as novel inhibitors of TNF-α converting enzyme using HPLC/GCMS analysis, FTIR and molecular modelling

Tumor Necrosis Factor Alpha Converting Enzyme (TACE) mediates inflammatory disorder and contributes to the pathophysiology of a variety of illnesses, such as chronic inflammation and cancer. This study identified metabolites in solvent extracts of Kigelia africana as putative TACE inhibitors due to the plant's known anti-inflammatory properties. HPLC-MS/GCMS analysis was used to characterize tentative phytochemicals from K. africana. The identified metabolites (n = 123) were docked with TACE to reveal the lead compounds. Binding free energy, ADMET prediction, molecular dynamics simulation at 100 ns, and DFT calculation were further conducted. The results revealed that K. africana contains sterol, phenols, alkaloids, terpenes and flavonoids. The FTIR shows that the extracts had peaks that correspond to the presence of different functional groups. The quantum polarized ligand docking (QPLD) analysis identified compound (n = 3) with binding affinity higher than standard compound IK-682. The hits also had modest ADMET profiles, interacted with essential residues within TACE binding pockets, and formed stable complexes with the protein. The 100 ns MD simulation shows that the compounds formed fairly stable interactions and complex with the protein as evidenced through RMSF, RMSD and MM-GBA results. The HOMO/LUMO, global descriptive molecular electrostatic potential Fukui function aid in the identification of the compounds' atomic sites prone to electrophilic/neutrophilic attacks, and non-covalent interactions. This study suggests that K. africana's bioactive compounds are capable of mitigating inflammation by inhibiting TACE.Communicated by Ramaswamy H. Sarma.

QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum

Cryptosporidium parvum (Cp) causes a gastro-intestinal disease called Cryptosporidiosis. C. parvum Inosine 5' monophosphate dehydrogenase (CpIMPDH) is responsible for the production of guanine nucleotides. In the present study, 37 known urea-based congeneric compounds were used to build a 2D and 3D QSAR model against CpIMPDH. The built models were validated based on OECD principles. A deep learning model was adopted from a framework called Deep Purpose. The model was trained with 288 known active compounds and validated using a test set. From the training set of the 3D QSAR, a pharmacophore model was built and the best pharmacophore hypotheses were scored and sorted using a phase-hypo score. A phytochemical database was screened using both the pharmacophore model and a deep learning model. The screened compounds were considered for glide XP docking, followed by quantum polarized ligand docking. Finally, the best compound among them was considered for molecular dynamics simulation study.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

The evaluation of QM/MM-driven molecular docking combined with MM/GBSA calculations as a halogen-bond scoring strategy

The combination of quantum mechanics/molecular mechanics-driven (QM/MM) molecular docking with binding free-energy calculations was successfully used to reproduce the X-ray geometries of protein–ligand complexes with halogen bonding. The procedure involves quantum-polarized ligand docking (QPLD) to obtain the QM-derived ligand atomic charges in the protein environment at the B3PW91/cc-pVTZ level and the MM/GBSA (generalized-Born/surface area) algorithm to calculate the binding free energies of resultant complexes. The performance was validated using a set of 106 X-ray complexes and compared with the Glide and AutoDock VinaXB scoring functions in terms of RMSD and the reconstruction of halogen-bond geometry (distance and σ-hole angle). The results revealed that docking and scoring using the QPLD–GBSA procedure outperformed the remaining scoring functions in the majority of instances. Additionally, a comparison of the orientation of the top ranked binding poses calculated using the fixed atomic charges of ligands obtained from force-field parameterization and by QM calculations in the protein environment provides strong evidence that the use of QM-derived charges is significant.

Applications of computer-aided approaches in the development of hepatitis C antiviral agents

INTRODUCTION

Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials. Areas covered: This review discusses the applications of different computational approaches in HCV drug design. Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

Inside HDACs with more selective HDAC inhibitors

Inhibitors of histone deacetylases (HDACs) are nowadays part of the therapeutic arsenal mainly against cancers, with four compounds approved by the Food and Drug Administration. During the last five years, several groups have made continuous efforts to improve this class of compounds, designing more selective compounds or compounds with multiple capacities. After a survey of the HDAC biology and structures, this review summarizes the results of the chemists working in this field, and highlights when possible the behavior of the molecules inside their targets.

Molecular recognition of naphthalene diimide ligands by telomeric quadruplex-DNA: the importance of the protonation state and mediated hydrogen bonds

How important are mediated hydrogen bonds in terms of molecular recognition? Compliance Constants (relaxed force constants) give the answer.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

A steered molecular dynamics mediated hit discovery for histone deacetylases

The inhibitors of class I histone deacetylases (HDACIs) have gained significant interest in cancer therapeutics. Virtual high throughput screening (vHTS) is one of the popular approaches used in the identification of novel scaffolds of HDACIs. However, an accurate description of ligand-protein flexibilities in the vHTS remains challenging. In this work, we implement an integrated approach, which combines the vHTS with the 'state-of-the-art' steered molecular dynamics (SMD). This approach serves as an efficient tool to identify potential hits and characterize their binding potencies against the class I HDACs in a flexible solvent environment. A hybrid pharmacophore-based and structure-based vHTS method identifies the hits with more favourable physico-chemical features against the class I HDACs. Our pharmacophore-based screening enhanced the quality of the vHTS outcomes. Further, the molecular interactions between the hits and the HDACs are investigated using the SMD-driven force profiles, which in turn resulted in filtering the hits with higher binding potencies against the HDACs. Our results, therefore, reveal that vHTS and SMD can be a complementary and effective analytical tool for accelerating the hit identification phase in structure-based drug design.

Molecular modeling studies and comparative analysis on structurally similar HTLV and HIV protease using HIV-PR inhibitors

Retroviruses are most perilous viral family, which cause much damage to the Homo sapiens. HTLV-1 mechanism found to more similar with HIV-1 and both retroviruses are causative agents of severe and fatal diseases including adult T-cell leukemia (ATL) and the acquired immune deficiency syndrome (AIDS). Both viruses code for a protease (PR) that is essential for replication and therefore represents a key target for drugs interfering with viral infection. In this work, the comparative study of HIV-1 and HTLV-1 PR enzymes through sequence and structural analysis is reported along with approved drugs of HIV-PR. Conformation of each HIV PR drugs have been examined with different parameters of interactions and energy scorings parameters. MD simulations with respect to timescale event of 20 ns favors that, few HIV-PR inhibitors can be more active inside the HTLV-1 PR binding pocket. Overall results suggest that, some of HIV inhibitors like Tipranavir, Indinavir, Darunavir and Amprenavir are having good energy levels with HTLV-1. Due to absence of interactions with MET37, here we report that derivatives of these compounds can be much better inhibitors for targeting HTLV-1 proteolytic activity.

Fragment based electronic structural analysis of l-phenylalanine using calculated ionization spectroscopy and dual space analysis

Fragmentation schemes in phenylalanine revealed using ionization spectroscopy and dual space analysis.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes.