A short survey of dengue protease inhibitor development in the past 6 years (2015-2020) with an emphasis on similarities between DENV and SARS-CoV-2 proteases.

Dengue remains a disease of significant concern, responsible for nearly half of all arthropod-borne disease cases across the globe. Due to the lack of potent and targeted therapeutics, palliative treatment and the adoption of preventive measures remain the only available options. Compounding the problem further, the failure of the only dengue vaccine, Dengvaxia®, also delivered a significant blow to any hopes for the treatment of dengue fever. However, the success of Human Immuno-deficiency Virus (HIV) and Hepatitis C Virus (HCV) protease inhibitors in the past have continued to encourage researchers to investigate other viral protease targets. Dengue virus (DENV) NS2B-NS3 protease is an attractive target partly due to its role in polyprotein processing and also for being the most conserved domain in the viral genome. During the early days of the COVID-19 pandemic, a few cases of Dengue-COVID 19 co-infection were reported. In this review, we compared the substrate-peptide residue preferences and the residues lining the sub-pockets of the proteases of these two viruses and analyzed the significance of this similarity. Also, we attempted to abridge the developments in anti-dengue drug discovery in the last six years (2015-2020), focusing on critical discoveries that influenced the research.

Atosiban and Rutin exhibit anti-mycobacterial activity - An integrated computational and biophysical insight toward drug repurposing strategy against Mycobacterium tuberculosis targeting its essential enzyme HemD.

With the advancements of high throughput computational screening procedures, drug repurposing became the privileged framework for drug discovery. The structure-based drug discovery is the widely used method of drug repurposing, consisting of computational screening of compounds and testing them in-vitro. This current method of repurposing leaves room for mechanistic insights into how these screened hits interact with and influence their targets. We addressed this gap in the current study by integrating highly sensitive biophysical methods into existing computational repurposing methods. We also corroborated our computational and biophysical findings on H37Rv for the anti-mycobacterial action of selected drugs in-vitro and ex-vivo conditions. Atosiban and Rutin were screened as highly interacting hits against HemD through multi-stage docking and were cross-validated in biophysical studies. The affinity of these drugs (K ~ 106 M-1) was quantified using fluorescence quenching studies. Differential Scanning Fluorimetry (DSF) and urea-based chemical denaturation studies revealed a destabilizing effect of these drugs on target which was further validated using MD simulations. Conformational rearrangements of secondary structures were established using CD spectra and intrinsic fluorescence. Furthermore, Atosiban and Rutin inhibited M.tb growth in-vitro and ex-vivo while remaining non-toxic to mice peritoneal macrophages.

Physicochemical parameters for design and development of lead herbicide molecules: Is 'Lipinski's rule of 5' appropriate for herbicide discovery?

BACKGROUND: Herbicide use has been a great add-on in agriculture, aiding weed management in crop fields, thereby escalating crop production. However, the development of resistance in weeds against the existing herbicides is a setback. The development of herbicide resistance has compelled the agrochemical industries to replace existing herbicides with novel agrochemicals. Developing new herbicide molecules through traditional methods is time-consuming and cost-prohibitive. The use of high-throughput virtual screening (HTVS) through physicochemical properties, de novo design and combinatorial design of molecules with cutting-edge computational methods is an alternative to the traditional techniques in lead molecule discovery. The lack of optimal physicochemical criteria for screening herbicide-like molecules has become a hindrance in the process. RESULTS: In this study, physicochemical parameters [molecular weight, aromatic atoms, rotatable bonds, hydrogen-bonding capacity, topological polar surface area (TPSA), polarity and solubility] of known herbicide molecules have been studied and evaluated, and optimal criteria have been proposed for target-specific herbicides. Properties including molecular weight and hydrogen (H)-bond acceptor atoms tend to have higher values, but the range of H-bond donor atoms is relatively lower. These are distinguishable characteristics in herbicides when compared with oral drugs. Significant variations in optimal physicochemical parameters between herbicides of different groups (targeting weeds with different modes of action) have been observed. CONCLUSION: The proposed parameters for respective target sites could be used as filters for in silico screening, designing and developing of target-specific lead herbicide molecules. © 2023 Society of Chemical Industry.

Virtual screening and multilevel precision-based prioritisation of natural inhibitors targeting the ATPase domain of human DNA topoisomerase II alpha.

Human DNA topoisomerase II alpha (hTopIIα) is a classic chemotherapeutic drug target. The existing hTopIIα poisons cause numerous side effects such as the development of cardiotoxicity, secondary malignancies, and multidrug resistance. The use of catalytic inhibitors targeting the ATP-binding cavity of the enzyme is considered a safer alternative due to the less deleterious mechanism of action. Hence, in this study, we carried out high throughput structure-based virtual screening of the NPASS natural product database against the ATPase domain of hTopIIα and identified the five best ligand hits. This was followed by comprehensive validation through molecular dynamics simulations, binding free energy calculation and ADMET analysis. On stringent multilevel prioritization, we identified promising natural product catalytic inhibitors that showed high binding affinity and stability within the ligand-binding cavity and may serve as ideal hits for anticancer drug development.Communicated by Ramaswamy H. Sarma.

High throughput virtual screening (HTVS) of peptide library: Technological advancement in ligand discovery.

High-throughput virtual screening (HTVS) is a leading biopharmaceutical technology that employs computational algorithms to uncover biologically active compounds from large-scale collections of chemical compound libraries. In addition, this method often leverages the precedence of screening focused libraries for assessing their binding affinities and improving physicochemical properties. Usually, developing a drug sometimes takes ages, and lessons are learnt from FDA-approved drugs. This screening strategy saves resources and time compared to laboratory testing in certain stages of drug discovery. Yet in-silico investigations remain challenging in some cases of drug discovery. For the last few decades, peptide-based drug discoveries have received remarkable momentum for several advantages over small molecules. Therefore, developing a high-fidelity HTVS platform for chemically versatile peptide libraries is highly desired. This review summarises the modern and frequently appreciated HTVS strategies for peptide libraries from 2011 to 2021. In addition, we focus on the software used for preparing peptide libraries, their screening techniques and shortcomings. An index of various HTVS methods reported here should assist researchers in identifying tools that could be beneficial for their peptide library screening projects.

Membrane dynamics simulation and virtual screening reveals potential dual natural inhibitors of endothelin receptors for targeting glaucomatous condition.

Glaucoma is the second leading cause of blindness in the world and is characterized by the loss of retinal ganglion cells (RGC) over a period of time, leading to complete blindness. Recently, endothelin has been identified as an important factor that influences intraocular pressure IOP, OBF, and direct RGC damage. Targeting the endothelin receptor signaling pathway in glaucoma is considered to be highly beneficial, as it can effectively modulate IOP, OBF, and RGC damage, the key factors which are essential to modulate the disease progression holistically. Currently, synthetic drugs like Bosentan, BQ-123, and prostaglandin analogues are available as endothelin receptor antagonists, which are extensively used in the treatment of cardiovascular and other conditions like systemic hypertension. However, the usage of these drugs in glaucoma is limited due to toxicity and poor bioavailability in the ocular milieu. Thus, there is a need for potential natural compounds as endothelin receptor antagonists that acts as dual inhibitors by targeting both ETA and ETB and are highly efficient with the least toxicity. Hence, this study is intended to prioritize endothelin receptor antagonists by structural bioinformatics approaches involving molecular modeling, molecular dynamics, and molecular docking studies. Subsequently, High throughput virtual screening (HTVS) vs. Natural compound databases targeting the optimal binding sites of both ETA and ETB. Following this, the common hits were subjected to binding free energy calculations (MMGBSA) and ADMETox analysis. Finally, the most potential hits were analyzed for MD based binding stability analysis and binding free energy. Similarly, the known synthetic inhibitors were also docked to the receptors and the results were analyzed. From this study, it was inferred that among the natural compounds dataset (8929 compounds), only 4 common compounds were identified as hits. Among these, only one compound ST075640 surpassed all the prioritization criteria including MMGBSA, ADMETox prediction, dual inhibitory potential (ETA & ETB), and also in structural comparative analysis with bosentan it showed similar efficiency. Thus, the validated hit shall prove to be effective in modulating endothelin mediated IOP, OBF, and RGC damage in glaucomatous condition.

TRAF2 and NCK-Interacting Kinase Inhibitors for Colorectal Cancer: In Vitro and Theoretical Validations.

TRAF2 and NCK-interacting kinase (TNIK) is a critical factor in colorectal cancer (CRC) proliferation mediated by Wnt signaling. We attempted to identify efficient TNIK inhibitors using computational high-throughput virtual screening (HTVS) from various drug banks and databases. By performing/on performing e-pharmacophore screening and molecular docking methods, from ∼700 000 molecules, compounds LC_222150, LC_112060, and LC_64796 were identified as potential leads, through molecular dynamics (MD) simulations and density functional theory (DFT). These top 3 structures were commercially procured, and their inhibitory activity was assessed in vitro. Significant TNIK inhibition was observed, with an average IC50 of 18.33 ± 0.75 nM. In terms of anticancer activity, the observed average relative % activity (RPA) of 90.28 ± 1.04 for these compounds compared well with doxorubicin (86.75 ± 1.45) as a standard. Compounds LC_222150, LC_112060, and LC_64796, therefore, warrant further evaluation in vivo to assess their CRC therapeutic effects.

Neutralization of SARS-CoV-2 Spike Protein via Natural Compounds: A Multilayered High Throughput Virtual Screening Approach.

BACKGROUND: Previously human society has faced various unprecedented pandemics in the history and viruses have majorly held the responsibilities of those outbreaks. Furthermore, due to amplified global connection and speedy modernization, epidemic outbreaks caused by novel and re-emerging viruses signify potential risk to community health. Despite great advancements in immunization and drug discovery processes, various viruses still lack prophylactic vaccines and efficient antiviral therapies. Although, vaccine is a prophylaxes option, but it cannot be applied to infected patients, hence therapeutic interventions are urgently needed to control the ongoing global SARS- CoV-2 pandemic condition. To spot the novel antiviral therapy is of decisive importance and Mother Nature is an excellent source for such discoveries. METHODOLOGY: In this article, prompt high through-put virtual screening for vetting the best possible drug candidates from natural compounds' databases has been implemented. Herein, time tested rigorous multi-layered drug screening process to narrow down 66,969 natural compounds for the identification of potential lead(s) is implemented. Druggability parameters, different docking approaches and neutralization tendency of the natural products were employed in this study to screen the best possible natural compounds from the digital libraries. CONCLUSION: The results of this study conclude that compounds PALA and HMCA are potential inhibitors of SARS-CoV-2 spike protein and can be further explored for experimental validation. Overall, the methodological approach reported in this article can be suitably used to find the potential drug candidates against SARS-CoV2 in the burning situation of COVID-19 with less expenditure and a concise span of time.

Group A streptococcus inhibitors by high-throughput virtual screening.

Group A streptococcus (GAS) is a Gram-positive bacterium, which can cause multiple types of disease from mild infections of skin and throat to invasive and life-threatening infections. Recently RNase J1 and J2 were found to be essential for the growth of GAS. In order to identify inhibitors against RNase J1/J2, homology models of both the ligand-free apo-form and the ligand-bound holo-form complexes were constructed as templates for high-throughput virtual screening (HTVS). A focused small molecule library and the commercially available Maybridge database were employed as sources for potential inhibitors. A cell-based biological assay identified two compounds with 10 µM MIC activity.