3D-QSAR-aided design of potent c-Met inhibitors using molecular dynamics simulation and binding free energy calculation

Computation-Directed Molecular Design, Synthesis, and Fungicidal Activity of Succinate Dehydrogenase Inhibitors

Succinate dehydrogenase inhibitors (SDHIs) are a class of fungicides targeting the pathogenic fungi mitochondrial SDH. Here, molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics (MD) simulations were used to guide SDHI innovation. Molecular docking was performed to explore the binding modes of SDH and its inhibitors. 3D-QSAR models were carried out on 33 compounds with activity against Rhizoctonia cerealis (R. cerealis); their structure-activity relationships were analyzed using comparative molecular field analysis and comparative molecular similarity indices analysis. MD simulations were used to assess the stability of the complexes under physiological conditions, and the results were consistent with molecular docking. Binding free energy was calculated through the molecular mechanics generalized born surface area method, and the binding free energy was decomposed. The results are consistent with the activity of bioassay and indicate that van der Waals and lipophilic interactions contribute the most in the molecular binding process. Afterward, we designed and synthesized 12 compounds under the guidance of the above-mentioned analyses, bioassay found that F9 was active against R. cerealis with the EC50 value of 9.43 μg/mL, and F4, F5, and F9 were active against Botrytis cinerea with an EC50 values of 5.80, 3.17, and 1.63 μg/mL, respectively. They all showed good activity between positive controls of pydiflumetofen and thifluzamide. Our study provides new considerations for effective SDHIs discovery.

Molecular and Biological Investigation of Isolated Marine Fungal Metabolites as Anticancer Agents: A Multi-Target Approach

Cancer is the leading cause of death globally, with an increasing number of cases being annually reported. Nature-derived metabolites have been widely studied for their potential programmed necrosis, cytotoxicity, and anti-proliferation leading to enrichment for the modern medicine, particularly within the last couple of decades. At a more rapid pace, the concept of multi-target agents has evolved from being an innovative approach into a regular drug development procedure for hampering the multi-fashioned pathophysiology and high-resistance nature of cancer cells. With the advent of the Red Sea Penicillium chrysogenum strain S003-isolated indole-based alkaloids, we thoroughly investigated the molecular aspects for three major metabolites: meleagrin (MEL), roquefortine C (ROC), and isoroquefortine C (ISO) against three cancer-associated biological targets Cdc-25A, PTP-1B, and c-Met kinase. The study presented, for the first time, the detailed molecular insights and near-physiological affinity for these marine indole alkaloids against the assign targets through molecular docking-coupled all-atom dynamic simulation analysis. Findings highlighted the superiority of MEL's binding affinity/stability being quite in concordance with the in vitro anticancer activity profile conducted via sulforhodamine B bioassay on different cancerous cell lines reaching down to low micromolar or even nanomolar potencies. The advent of lengthy structural topologies via the metabolites' extended tetracyclic cores and aromatic imidazole arm permitted multi-pocket accommodation addressing the selectivity concerns. Additionally, the presence decorating polar functionalities on the core hydrophobic tetracyclic ring contributed compound's pharmacodynamic preferentiality. Introducing ionizable functionality with more lipophilic characters was highlighted to improve binding affinities which was also in concordance with the conducted drug-likeness/pharmacokinetic profiling for obtaining a balanced pharmacokinetic/dynamic profile. Our study adds to the knowledge regarding drug development and optimization of marine-isolated indole-based alkaloids for future iterative synthesis and pre-clinical investigations as multi-target anticancer agents.

Exploration of the Binding Modes of Toll-Like Receptor 4 Competitive Inhibitors: A Combined Ligand-Based and Target-Based Approach

The interactions of Toll-like receptor 4 (TLR4) with competitive inhibitors were investigated by a combined ligand-based and target-based approach. Firstly, the ligand-based pharmacophore model of the reported TLR4 inhibitors was constructed by utilizing the common feature method, which included three hydrophobic groups and a hydrogen bond receptor. The Schrödinger software suite glide module was used to dock inhibitors with proteins and verify the importance of these four interaction points from the target level. Then, molecular dynamics, alanine scanning mutagenesis, and binding free energy calculation were used to identify the key amino acids in the binding mode. In addition, blind docking proved that the TLR4 inhibitor does not bind to TLR4 itself like other TLR family proteins. Based on this, we also screened a class of sesquiterpene coumarins which possibly have TLR4 inhibitory activity and will conduct a detailed study later. Together, this study revealed the interactions between TLR4 protein and its competitive inhibitors, which shed light on better access for developing its novel inhibitors.

Promising inhibitors of main protease of novel corona virus to prevent the spread of COVID-19 using docking and molecular dynamics simulation

Coronavirus disease-2019 (COVID-19) is a global health emergency and the matter of serious concern, which has been declared a pandemic by WHO. Till date, no potential medicine/ drug is available to cure the infected persons from SARS-CoV-2. This deadly virus is named as novel 2019-nCoV coronavirus and caused coronavirus disease, that is, COVID-19. The first case of SARS-CoV-2 infection in human was confirmed in the Wuhan city of the China. COVID-19 is an infectious disease and spread from man to man as well as surface to man . In the present work, in silico approach was followed to find potential molecule to control this infection. Authors have screened more than one million molecules available in the ZINC database and taken the best two compounds based on binding energy score. These lead molecules were further studied through docking against the main protease of SARS-CoV-2. Then, molecular dynamics simulations of the main protease with and without screened compounds were performed at room temperature to determine the thermodynamic parameters to understand the inhibition. Further, molecular dynamics simulations at different temperatures were performed to understand the efficiency of the inhibition of the main protease in the presence of the screened compounds. Change in energy for the formation of the complexes between the main protease of novel coronavirus and ZINC20601870 as well ZINC00793735 at room temperature was determined on applying MM-GBSA calculations. Docking and molecular dynamics simulations showed their antiviral potential and may inhibit viral replication experimentally. Communicated by Ramaswamy H. Sarma.

Enhanced metastable state models of TAM kinase binding to cabozantinib explains the dynamic nature of receptor tyrosine kinases

Receptor tyrosine kinases (RTKs) are essential proteins in the regulation of cell signaling. Tyro3, Axl and Mer are members of TAM RTKs and are overexpressed in several cancer forms. Kinase inhibitors such as cabozantinib, foretinib are reported to inhibit TAM kinases at nanomolar concentrations. The atomistic details of structure and mechanism of functional regulation is required to understand their normal physiological process and when bound to an inhibitor. The docking of cabozantinib into the active state conformations of TAM kinases (crystal structure and computational models) revealed the best binding pose and the complex formation that is mediated through non-bonding interactions involving the hinge region residues. The alterations in the conformations and the regions of flexibility in apo and complexed TAM kinases as a course of time are studied using 250 ns molecular dynamics (MD) simulations. The post-MD trajectory analysis using Python libraries like ProDy, MDTraj and PyEMMA revealed encrypted protein dynamic motions in active kinetic metastable states. Comparison between Principal component analysis and Anisotropic mode analysis deciphered structural residue interactions and salt bridge contacts between apo and inhibitor bound TAM kinases. Various structural changes occurred in αC-helix and activation loop involving hydrogen bonding between residues from Lys-(β3 sheet), Glu-(αC-helix) and Asp-(DFG-motif) resulting in higher RMSD. Mechanical stiffness plots revealed that similar regions in apo and cabozantinib bound Axl fluctuated during MD simulations whereas different regions in Tyro3 and Mer kinases. The residue interaction network plots revealed important salt bridges that lead to constrained domain motions in the TAM kinases.Communicated by Ramaswamy H. Sarma.

Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected—thanks to the molecular docking results—were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC50 of 7.2 μM. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

Computational Approaches for the Design of (Mutant-)Selective Tyrosine Kinase Inhibitors: State-of-the-Art and Future Prospects

Kinases remain one of the major attractive therapeutic targets for a large number of indications such as cancer, rheumatoid arthritis, cardiac failure and many others. Design and development of kinase inhibitors (ATP-competitive, allosteric or covalent) is a clinically validated and successful strategy in the pharmaceutical industry. The perks come with limitations, particularly the development of resistance to highly potent and selective inhibitors. When this happens, the cycle needs to be repeated, i.e., the design and development of kinase inhibitors active against the mutated forms. The complexity of tumor milieu makes it awfully difficult for these molecularly-targeted therapies to work. Every year newer and better versions of these agents are introduced in the clinic. Several computational approaches such as structure-, ligand-based or hybrid ones continue to live up to their potential in discovering novel kinase inhibitors. New schools of thought in this area continue to emerge, e.g., development of dual-target kinase inhibitors. But there are fundamental issues with this approach. It is indeed difficult to selectively optimize binding at two entirely different or related kinases. In addition to the conventional strategies, modern technologies (machine learning, deep learning, artificial intelligence, etc.) started yielding the results and building success stories. Computational tools invariably played a critical role in catalysing the phenomenal progress in kinase drug discovery field. The present review summarized the progress in utilizing computational methods and tools for discovering (mutant-)selective tyrosine kinase inhibitor drugs in the last three years (2017-2019). Representative investigations have been discussed, while others are merely listed. The author believes that the enthusiastic reader will be inspired to dig out the cited literature extensively to appreciate the progress made so far and the future prospects of the field.