Predicting the biological activities through QSAR analysis and docking-based scoring

Multiple integrated computational approach to analyse wound healing potential of Symplocos racemosa bark as Matrix metalloproteinase inhibitors

The healing of wounds is the flagging concern in chronic wound cases especially when accompanied by pathogenic, diabetic comorbidities. Matrix metalloproteinases are associated with widespread pathological ailments, and the selective inhibitors for metalloproteinases can be of great interest in wound healing strategies. In the present research study, six constituents of Symplocos racemosa Roxb were evaluated for the docking aptitudes on human matrix metalloproteinase MMP 2 (PDB ID: 1QIB) and MMP 9 (PDB ID: 4H1Q) utilising Autodock Vina followed by the visualisation using Discovery studio (DS). The Pymol was used to generate the poses and the best binding pose was chosen for the docking aptitudes. 2D interactions and the 3D poses of the docked complex were accomplished using DS and LigPlot + software respectively. Working on SWISS ADME and OSIRIS software accomplished the physicochemical characteristics, absorption, distribution, metabolism, excretion, molecular properties, bioactivity score, and toxicity predictions. The molecule's physiochemical investigations discovered that all of the ligands comply with Lipinski's rule of five except compound 6, which deviated with two violations. Docking studies against 4H1Q revealed that compounds 1, 3, 5 and 6 exhibited maximum interactions with the target protein, with the free binding energies of -8.3 kJ Mol-1, -9.3 kJ Mol-1, -7.2 kJ Mol-1 and -11.0 kJ Mol-1 respectively. In case of the 1QIB target, compounds 1, 3 and 6 displayed remarkable binding energies of -8.7 kJ mol-1, -9.0 kJ mol-1 and -8.8 kJ mol-1. Bioactivity prediction study revealed that all of the selected Phytoconstituents displayed incredible Bioactivity scores. None of the selected chemical compounds was found to be irritant to the skin as discovered by toxicity studies. The contacts of the ligand-protein complex during the simulation studies revealed that the H-bond interactions of the ligands with LEU188, ALA189, GLN402, ARG420, MET422, PRO421, and ARG424 of 4H1Q were stable for more than 30% of the simulation time. It was thus concluded that the tested compounds predominantly compounds 1, 5 and 6 might rank among the vital supplementary lead drugs in chronic wounds and healing complexities. It is also worth noting the potential aptitude of the compound 3, however, its toxicity concern must be considered.

QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure–toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient (R²) and a leave-one-out coefficient (Q²_LOO) of 0.6584 and 0.6289, respectively.

Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research

BACKGROUND

Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process.

OBJECTIVE

This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively.

METHODS

Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection.

RESULTS

Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates.

CONCLUSION

This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.

Interrogation of Bacillus anthracis SrtA active site loop forming open/close lid conformations through extensive MD simulations for understanding binding selectivity of SrtA inhibitors

Bacillus anthracis is a gram positive, deadly spore forming bacteria causing anthrax and these bacteria having the complex mechanism in the cell wall envelope, which can adopt the changes in environmental conditions. In this, the membrane bound cell wall proteins are said to progressive drug target for the inhibition of Bacillus anthracis. Among the cell wall proteins, the SrtA is one of the important mechanistic protein, which mediate the ligation with LPXTG motif by forming the amide bonds. The SrtA plays the vital role in cell signalling, cell wall formation, and biofilm formations. Inhibition of SrtA leads to rupture of the cell wall and biofilm formation, and that leads to inhibition of Bacillus anthracis and thus, SrtA is core important enzyme to study the inhibition mechanism. In this study, we have examined 28 compounds, which have the inhibitory activity against the Bacillus anthracis SrtA for developing the 3D-QSAR and also, compounds binding selectivity with both open and closed SrtA conformations, obtained from 100 ns of MD simulations. The binding site loop deviate in forming the open and closed gate mechanism is investigated to understand the inhibitory profile of reported compounds, and results show the closed state active site conformations are required for ligand binding specificity. Overall, the present study may offer an opportunity for better understanding of the mechanism of action and can be aided to further designing of a novel and highly potent SrtA inhibitors.

Anti-SARS-CoV Natural Products With the Potential to Inhibit SARS-CoV-2 (COVID-19)

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), known to cause the disease COVID-19, was declared a pandemic in early 2020. The objective of this review was to collate information regarding the potential of plants and natural products to inhibit coronavirus and targets associated with infection in humans and to highlight known drugs, which may have potential activity against SARS-CoV-2. Due to the similarity in the RNA genome, main proteases, and primary host receptor between SARS-CoV and SARS-CoV-2, a review was conducted on plants and secondary metabolites, which have shown activity against SARS-CoV. Numerous scientific reports on the potential of plants and secondary metabolites against SARS-CoV infection were found, providing important information on their possible activity against SARS-CoV-2. Based on current literature, 83 compounds have been identified with the potential to inhibit COVID-19. The most prominent selectivity was found for the alkaloid, lycorine, the lignan, savinin, and the abietane terpenoid, 8-beta-hydroxyabieta-9(11),13-dien-12-one with selectivity index values greater than 945, 667, and 510, respectively. Plants and their secondary metabolites, with activity against targets associated with the SARS-CoV infection, could provide valuable leads for the development into drugs for the novel SARS-CoV-2. The prospects of using computational methods to screen secondary metabolites against SARS-CoV targets are briefly discussed, and the drawbacks have been highlighted. Finally, we discuss plants traditionally used in Southern Africa for symptoms associated with respiratory viral infections and influenza, such as coughs, fever, and colds. However, only a few of these plants have been screened against SARS-CoV. Natural products hold a prominent role in discovering novel therapeutics to mitigate the current COVID-19 pandemic; however, further investigations regarding in vitro, in vivo, pre-clinical, and clinical phases are still required.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Computational studies to predict or explain G protein coupled receptor polypharmacology

Since G protein-coupled receptors (GPCRs) belong to a very large superfamily of evolutionarily related receptors (>800 members in humans), and due to the rapid progress on their structural biology, they are ideal candidates for polypharmacology studies. Broad screening and bioinformatics/chemoinformatics have been applied to understanding off-target effects of GPCR ligands. It is now feasible to approach the question of GPCR polypharmacology using molecular modeling and the available X-ray GPCR structures. As an example, large and sterically constrained adenosine derivatives (potent adenosine receptor ligands with low conformational freedom and multiple extended substituents) were screened for binding at diverse receptors. Unanticipated off-target interactions, including at biogenic amine receptors, were then modeled using a structure-based approach to provide a consistent understanding of recognition. A conserved Asp in TM3 changed its role from counterion for biogenic amines to characteristic H-bonding to adenosine. The same systematic approach could potentially be applied to many GPCRs or other receptors using other sets of congeneric ligands.

In-silico analyses of sesquiterpene-related compounds on selected Leishmania enzyme-based targets

A great number of sesquiterpenes are reported in the available literature as good antileishmanial leads. However, their mode of action at the molecular level has not been elucidated. The lack of molecular studies could be considered an impediment for studies seeking to improve sesquiterpene-based drug design. The present in silico study allows us to make important observations about the molecular details of the binding modes of a set of antileishmanial sesquiterpenes against four drug-enzyme targets [pteridine reductase-1 (PTR1), N-myristoyl transferase (NMT), cysteine synthase (CS), trypanothione synthetase (TryS)]. Through molecular docking it was found that two sesquiterpene coumarins are promising leads for the PTR1 and TryS inhibition purposes, and some xanthanolides also exhibited better affinity towards PTR1 and CS binding. In addition, the affinity values were clustered by Principal Component Analysis and drug-like properties were analyzed for the strongest-docking sesquiterpenes. The results are an excellent starting point for future studies of structural optimization of this kind of compounds.