Novel α-glucosidase Inhibitors Designed as Type 2 Diabetes Drugs by QSAR, Molecular Docking and Molecular Dynamics Simulation Methods

Diabetes mellitus is a globally prevalent disease of significant concern. Alpha-glucosidase has emerged as a prominent target for the treatment of type 2 diabetes. In this study, 39 α-glucosidase inhibitors (AGIs) of tetrahydrobenzo[b]thiophene-2-ylurea derivatives to establish a stable and valid Topomer CoMFA model, with a cross-validation coefficient (q2) of 0.766 and a non-cross-validation coefficient (r2) of 0.960. Subsequently, the ZINC15 database was used to screen the fragments, based on which 13 novel inhibitor molecules with theoretically potentially high activity were designed. Molecular docking and molecular dynamics simulations to understand the binding status of the inhibitor molecules to the target proteins showed that amino acids ASP215, GLN279 and ARG442 may form hydrogen bonds with the ligands and therefore enhance the inhibitory effect of the small molecules. Additionally, MM/PBSA calculations indicate that the newly designed molecules exhibit more stable binding modes. These molecules also demonstrate favorable ADMET properties with potential as AGIs. The findings would provide valuable guidance and a theoretical foundation for the design and development of novel AGIs.

Molecular docking and molecular dynamic simulation studies to identify potential terpenes against Internalin A protein of Listeria monocytogenes

Introduction

Ever since the outbreak of listeriosis and other related illnesses caused by the dreadful pathogen Listeria monocytogenes, the lives of immunocompromised individuals have been at risk.

Objectives and Methods

The main goal of this study is to comprehend the potential of terpenes, a major class of secondary metabolites in inhibiting one of the disease-causing protein Internalin A (InlA) of the pathogen via in silico approaches.

Results

The best binding affinity value of -9.5 kcal/mol was observed for Bipinnatin and Epispongiadiol according to the molecular docking studies. The compounds were further subjected to ADMET and biological activity estimation which confirmed their good pharmacokinetic properties and antibacterial activity.

Discussion

Molecular dynamic simulation for a timescale of 100 ns finally revealed Epispongiadiol to be a promising drug-like compound that could possibly pave the way to the treatment of this disease.

Discovery of novel BRD4-BD2 inhibitors via in silico approaches: QSAR techniques, molecular docking, and molecular dynamics simulations

Bromodomain-containing protein 4(BRD4) plays an important role in the occurrence and development of various malignant tumors, which has attracted the attention of scientific research institutions and pharmaceutical companies. The structural modification of most currently available BRD4 inhibitors is relatively simple, but the drug effectiveness is limited. Research has found that the inhibition of BD1 may promote the differentiation of oligodendrocyte progenitor cell; however, the inhibition of BD2 will not cause this outcome. Therefore, newly potential drugs which target BRD4-BD2 need further research. Herein, we initially built QSAR models out of 49 compounds using HQSAR, CoMFA, CoMSIA, and Topomer CoMFA technology. All of the models have shown suitable reliabilities (q2 = 0.778, 0.533, 0.640, 0.702, respectively) and predictive abilities (r2pred = 0.716, 0.6289, 0.6153, 0.7968, respectively) for BRD4-BD2 inhibitors. On the basis of QSAR results and the search of the R-group in the topomer search module, we designed 20 new compounds with high activity that showed appropriate docking score and suitable ADMET. Docking studies and MD simulation were carried out to reveal the amino acid residues (Asn351, Cys347, Tyr350, Pro293, and Asp299) at the active site of BRD4-BD2. Free energy calculations and free energy landscapes verified the stable binding results and indicated stable conformations of the complexes. These theoretical studies provide guidance and theoretical basis for designing and developing novel BRD4-BD2 inhibitors.

In silico studies of Mpro and PLpro from SARS-CoV-2 and a new class of cephalosporin drugs containing 1,2,4-thiadiazole

The SARS-CoV-2 proteases Mpro and PLpro are important targets for the development of antivirals against COVID-19. The functional group 1,2,4-thiadiazole has been indicated to inhibit cysteinyl proteases, such as papain and cathepsins. Of note, the 1,2,4-thiadiazole moiety is found in a new class of cephalosporin FDA-approved antibiotics: ceftaroline fosamil, ceftobiprole, and ceftobiprole medocaril. Here we investigated the interaction of these new antibiotics and their main metabolites with the SARS-CoV-2 proteases by molecular docking, molecular dynamics (MD), and density functional theory (DFT) calculations. Our results indicated the PLpro enzyme as a better in silico target for the new antibacterial cephalosporins. The results with ceftaroline fosamil and the dephosphorylate metabolite compounds should be tested as potential inhibitor of PLpro, Mpro, and SARS-CoV-2 replication in vitro. In addition, the data here reported can help in the design of new potential drugs against COVID-19 by exploiting the S atom reactivity in the 1,2,4-thiadiazole moiety.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11224-022-02036-5.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Theoretically exploring selective-binding mechanisms of BRD4 through integrative computational approaches

The origin of cancer is related to the dysregulation of multiple signal pathways and of physiological processes. Bromodomain-containing protein 4 (BRD4) has become an attractive target for the development of anticancer and anti-inflammatory agents since it can epigenetically regulate the transcription of growth-promoting genes. The synthesized BRD4 inhibitors with new chemical structures can reduce the drug resistance, but their binding modes and the inhibitory mechanism remain unclear. Here, we initially constructed robust QSAR models based on 68 reported tetrahydropteridin analogues using topomer CoMFA and HQSAR. On the basis of QSAR results, we designed 16 novel tetrahydropteridin analogues with modified structures and carried out docking studies. Instead of significant hydrogen bondings with amino acid residue Asn140 as reported in previous research, the molecular docking modelling suggested a novel docking pose that involves the amino acid residues (Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asp144, and Ile146) at the active site of BRD4. The MD simulations, free energy calculations, and residual energy contributions all indicate that hydrophobic interactions are decisive factors affecting bindings between inhibitors and BRD4. The current study provides new insights that can aid the discovery of BRD4 inhibitors with enhanced anti-cancer ability.

Computational strategies towards developing novel SARS-CoV-2 Mpro inhibitors against COVID-19

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains to be a serious threat due to the lack of a specific therapeutic agent. Computational methods are particularly suitable for rapidly fight against SARS-CoV-2. This present research aims to systematically explore the interaction mechanism of a series of novel bicycloproline-containing SARS-CoV-2 M^pro inhibitors through integrated computational approaches. We designed six structurally modified novel SARS-CoV-2 M^pro inhibitors based on the QSAR study. The four designed compounds with higher docking scores were further explored through molecular docking, molecular dynamics (MD) simulations, free energy calculations, and residual energy contributions estimated by the MM-PBSA approach, with comparison to compound 23(PDB entry 7D3I). This research not only provides robust QSAR models as valuable screening tools for the development of anti-COVID-19 drugs, but also proposes the newly designed SARS-CoV-2 M^pro inhibitors with nanomolar activities that can be potentially used for further characterization to treat SARS-CoV-2 virus.