Binding and inhibitory effect of ravidasvir on 3CLpro of SARS-CoV-2: a molecular docking, molecular dynamics and MM/PBSA approach

Identification of apigenin-4'-glucoside as bacterial DNA gyrase inhibitor by QSAR modeling, molecular docking, DFT, molecular dynamics, and in vitro confirmation studies

CONTEXT

It is well known that antibiotic resistance is a major health hazard. To eradicate antibiotic-resistant bacterial infections, it is essential to find a novel antibacterial agent. Hence, in this study, a quantitative structure-activity relationship (QSAR) model was developed using 43 DNA gyrase inhibitors, and 700 natural compounds were screened for their antibacterial properties. Based on molecular docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, the top three leads viz., apigenin-4'-glucoside, 8-deoxygartanin, and cryptodorine were selected and structurally optimized using density functional theory (DFT) studies. The optimized structures were redocked, and molecular dynamic (MD) simulations were performed. Binding energies were calculated by molecular mechanics/Poisson-Boltzmann surface area solvation (MM-PBSA). Based on the above studies, apigenin-4'-glucoside was identified as a potent antibacterial lead. Further in vitro confirmation studies were performed using the plant Lawsonia inermis containing apigenin-4'-glucoside to confirm the antibacterial activity.

METHODS

For QSAR modeling, 2D descriptors were calculated by PaDEL-Descriptors v2.21 software, and the model was developed using the DTClab QSAR tool. Docking was performed using PyRx v0.8 software. ORCA v5.0.1 computational package was used to optimize the structures. The job type used in optimization was equilibrium structure search using the DFT hybrid functional ORCA method B3LYP. The basis set was 6-311G (3df, 3pd) plus four polarization functions for all atoms. Accurate docking was performed for optimized leads using the iGEMDOCK v2.1 tool with a genetic algorithm by 10 solutions each of 80 generations. Molecular dynamic simulations were performed using GROMACS 2020.04 software with CHARMM36 all-atom force field.

Screening of potent inhibitor from Aquilaria malaccensis Lam. against arachidonic inflammatory enzymes: an insight from molecular docking, ADMET, molecular dynamics simulation and MM-PBSA approaches

The three primary enzymes COX (cyclooxygenase), LOX (lipoxygenase) and CYT-P450 (cytochrome P450), which are part of the arachidonic inflammatory pathway, play crucial role in the development of asthma, rheumatoid arthritis and cardiovascular diseases. Ethnomedicinally, plant-derived chemicals have a major role in the treatment of fatal illnesses. Aquilaria malaccensis Lam. widely known as agarwood is prized for its fragrance and therapeutic properties. The phytochemicals and extracts of this plant have significant healing properties in the treatment of serious illnesses. In the current work, an in-silico approach including molecular docking, ADMET (absorption, distribution, metabolism, excretion and toxicity), molecular dynamics (MD) simulation and molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) was performed to screen 33 bioactive compounds from this plant against COX-2 and 5-LOX in order to find the most effective inhibitor. 2-(2-Phenylethyl)chromone was found to inhibit both 5-LOX and COX-2, showing the highest binding affinities (-9.1 kcal/mol and -9.0 kcal/mol, respectively) than standard Ibuprofen and nordihydroguaiaretic acid (NDGA). 2-(2-Phenylethyl)chromone showed the highest drug-likeness score and low risk of toxicity compared to other phytochemicals. MD modeling and MM-PBSA calculations showed that 2-(2-Phenylethyl)chromone had a strong persistent binding interaction with 5-LOX than COX-2, and this interaction is comparable to the bounded standards Ibuprofen and NDGA. From this study, we may infer that the 2-(2-Phenylethyl)chromone can serve as a potent inhibitor and has scope to be employed in the treatment of inflammatory ailments.Communicated by Ramaswamy H. Sarma.

Unveiling the antiviral potential of Plant compounds from the Meliaceae family against the Zika virus through QSAR modeling and MD simulation analysis

Zika virus (ZIKV) is a flavivirus transmitted by mosquitoes, causing neurological disorders and congenital malformations. RNA-dependent RNA polymerase (RdRp) is one of its essential enzymes and a promising drug target for antiviral therapy due to its involvement in the growth and multiplication of the virus. In this study, we conducted a QSAR-based chemical library screening from the Meliaceae family to identify potential RdRp inhibitors. The QSAR model was built using the known inhibitors of RdRp NS5 of ZIKV and their biological activity (EC50), along with the structural and chemical characteristics of the compounds. The top two hit compounds were selected from QSAR screening for further analysis using molecular docking to evaluate their binding energies and intermolecular interactions with RdRp, including the critical residue Trp485. Furthermore, molecular dynamics (MD) simulations were performed to evaluate their binding stability and flexibility upon binding to RdRp. The MD results showed that the selected compounds formed stable complexes with RdRp, and their binding interactions were similar to those observed for the native ligand. The binding energies of the top two hits (-8.6 and -7.7 kcal/mole) were comparable to those of previously reported ZIKV RdRp inhibitors (-8.9 kcal/mole). The compound IMPHY009135 showed the strongest binding affinity with RdRp, forming multiple hydrogen bonds and hydrophobic interactions with key residues. However, compound IMPHY009276 showed the most stable and consistent RMSD, which was similar to the native ligand. Our findings suggest that IMPHY009135 and IMPHY009276 are potential lead compounds for developing novel antiviral agents against ZIKV.Communicated by Ramaswamy H. Sarma.

Conformation and Affinity Modulations by Multiple Phosphorylation Occurring in the BIN1 SH3 Domain Binding Site of the Tau Protein Proline-Rich Region

An increase in phosphorylation of the Tau protein is associated with Alzheimer's disease (AD) progression through unclear molecular mechanisms. In general, phosphorylation modifies the interaction of intrinsically disordered proteins, such as Tau, with other proteins; however, elucidating the structural basis of this regulation mechanism remains challenging. The bridging integrator-1 gene is an AD genetic determinant whose gene product, BIN1, directly interacts with Tau. The proline-rich motif recognized within a Tau(210-240) peptide by the SH3 domain of BIN1 (BIN1 SH3) is defined as ₂₁₆PTPP₂₁₉, and this interaction is modulated by phosphorylation. Phosphorylation of T217 within the Tau(210-240) peptide led to a 6-fold reduction in the affinity, while single phosphorylation at either T212, T231, or S235 had no effect on the interaction. Nonetheless, combined phosphorylation of T231 and S235 led to a 3-fold reduction in the affinity, although these phosphorylations are not within the BIN1 SH3-bound region of the Tau peptide. Using nuclear magnetic resonance (NMR) spectroscopy, these phosphorylations were shown to affect the local secondary structure and dynamics of the Tau(210-240) peptide. Models of the (un)phosphorylated peptides were obtained from molecular dynamics (MD) simulation validated by experimental data and showed compaction of the phosphorylated peptide due to increased salt bridge formation. This dynamic folding might indirectly impact the BIN1 SH3 binding by a decreased accessibility of the binding site. Regulation of the binding might thus not only be due to local electrostatic or steric effects from phosphorylation but also to the modification of the conformational properties of Tau.

Structural screening into the recognition of a potent inhibitor against non-structural protein 16: a molecular simulation to inhibit SARS-CoV-2 infection

COVID-19 infection is caused by endemic crown infection (SARS-CoV-2) and is associated with lung damage and severe immune response. Non-Structural Proteins are the central components of coronaviral transcription and replication machinery in SARS-CoV-2 and also stimulate mRNA cap methylation to avoid the immune response. Non-Structural Protein 16 (NSP16) is one of the primary targets for the drug discovery of coronaviruses. Discovering an effective inhibitor against the NSP16 in comparison with Sinefungin was the main purpose of this investigation. Binding free-energy calculations, computational methods of molecular dynamics, docking, and virtual screening were utilized in this study. The ZINC and PubChem databases were applied to screen some chemical compounds regarding Sinefungin as a control inhibitor. Based on structural similarity to Sinefungin, 355 structures were obtained from the mentioned databases. Subsequently, this set of compounds were monitored by AutoDock Vina software, and ultimately the potent inhibitor (PUBCHEM512713) was chosen. At the next stage, molecular dynamics were carried out by GROMACS software to evaluate the potential elected compounds in a simulated environment and in a timescale of 100 nanoseconds. MM-PBSA investigation exhibited that the value of binding free energy for PUBCHEM512713 (-30.829 kJ.mol^-1) is more potent than Sinefungin (-11.941 kJ.mol^-1). Furthermore, the results of ADME analysis illustrated that the pharmacokinetics, drug-likeness, and lipophilicity parameters of PUBCHEM512713 are admissible for human utilization. Finally, our data suggested that PUBCHEM512713 is an effective drug candidate for inhibiting the NSP16 and is suitable for in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

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.

Impact of Chromate and Dichromate on Lysozyme Stability: A Spectroscopic and Molecular Docking Investigation

A comparative study of interaction between Lysozyme (Lyz) with two hexavalent chromate ions; chromate and dichromate; which are prevalently known for their toxicity, was investigated using different spectroscopic techniques along with molecular docking study. Both steady-state and time-resolved studies revealed that the addition of chromate/dichromate is responsible for strong quenching of intrinsic fluorescence in Lyz and the quenching is caused by both static and dynamic quenching mechanism. Different binding and thermodynamic parameters were also calculated at different temperatures from the intrinsic fluorescence of Lyz. The conformational change in Lyz and thermodynamic parameters obtained during the course of interaction with chromate/dichromate were well-supported by the molecular docking results.

Efficacy and safety of sofosbuvir plus daclatasvir or ravidasvir in patients with COVID-19: A randomized controlled trial

Only a few treatments are approved for coronavirus disease‐2019 (COVID‐19) infections, with continuous debate about their clinical impact. Repurposing antiviral treatments might prove the fastest way to identify effective therapy. This trial aimed to evaluate the efficacy and safety of sofosbuvir (SOF) plus daclatasvir (DCV) or ravidasvir (RDV) added to standard care (SOC) for patients with moderate and severe COVID‐19 infection. Multicentre parallel randomized controlled open‐label trial. One hundred and twenty eligible patients with moderate and severe COVID‐19 infection were randomized to one of the study arms. Ten days of treatment with SOF plus DCV or RDV in addition to the standard of care compared to SOC. Follow up in 7 days. Sum of the counted symptoms at 7 and 10 days, mean change in oxygen saturation level, viral negativity, and rate of intensive care unit (ICU) admission. Compared to SOC, the SOF‐DCV group experienced a significantly lower sum of the counted symptoms (fever, headache, generalized aches, or respiratory distress) combined with no evidence of deterioration (ICU admission and mechanical ventilation) on Days 7 and 10 of treatment. Oxygen saturation also significantly improved among the SOF‐DCV group compared to SOC starting from Day 4. The study also showed positive trends regarding the efficacy of SOF‐DCV with a lower incidence of mortality. On the other hand, adding SOF‐RDV to SOC did not show significant improvements in endpoints. The results support the efficacy and safety of SOF‐DCV as an add‐on to SOC for the treatment of moderate to severe COVID‐19 infections.