Drug repurposing for SARS-CoV-2 main protease: Molecular docking and molecular dynamics investigations

Network pharmacology integrated molecular docking of fucoidan against oral cancer and in vitro evaluation- A study using GEO datasets

Oral cancer is a widespread health concern in rural India due to a lack of awareness, delayed diagnosis and limited access to affordable treatment options. The current chemotherapy has notable side effects, underscoring the need for new drug candidates with improved bioavailability and specificity. In this current research, fucoidan, a sulphated polysaccharide, was extracted from the brown algae Spatoglossum asperum, and shown to be cytotoxic in vitro against oral cancer cells (KB cell line) at an IC50 of 107.76 µg/ml, suggesting its potential as a drug candidate. This study further aimed to explore the potential therapeutic implications of fucoidan in managing oral cancer using network pharmacology. PharmMapper, Comparative Toxicogenomics Database and SuperPred were initially used to identify fucoidan protein targets. The identified targets were further screened against Gene Expression Omnibus (GSE23558, GSE25099 and GSE146483), OMIM, TCGA and GeneCards datasets to identify oral cancer-specific protein targets. The interactions between the selected proteins were visualised using STRING and Cytoscape. Subsequently, Database for Annotation, Visualization and Integrated Discovery was used for gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of candidate targets. The cancer-related network was assessed using CancerGeneNet, while life expectancy based on the expression of the top 10 CytoHubba ranked hub genes was evaluated using Kaplan-Meier plots. Finally, EGFR, AKT1, HSP90AA1 and SRC were selected for docking and molecular dynamics simulation with fucoidan, using Maestro and GROMACS, respectively.Communicated by Ramaswamy H. Sarma.

Targeting the omicron variant of SARS-CoV-2 with phytochemicals from Saudi medicinal plants: molecular docking combined with molecular dynamics investigations

The new health crises caused by SARS-CoV-2 have resulted in millions of deaths worldwide. First discovered in November 2021, the omicron variant is more transmissible and is able to evade the immune system better than other previously identified SARS-CoV-2 variants, leading to a spike in cases. Great efforts have been made to discover inhibitors against SARS-CoV-2. Main protease (Mpro) inhibitors are considered promising anti-SARS-CoV-2 agents. The U.S. FDA has issued an Emergency Use Authorization for ritonavir-boosted nirmatrelvir. Nirmatrelvir is the first orally bioavailable inhibitor of SARS-CoV-2 Mpro. There is an urgent need to monitor the mutations and solve the problem of resistance, especially omicron Mpro, which contains one mutation - P132H. In the present study, 132,57 phytochemicals from 80 medicinal plants grown in Saudi Arabia were docked into the active site of Mpro omicron variant. Free binding energies were also calculated. This led to the discovery of five phytochemicals that showed better docking scores than the bound ligand nirmatrelvir. In addition, these molecules exhibited favorable free binding energies. The stability of compounds 1-5 with the protein was studied using molecular dynamics (MD) simulations. These compounds showed acceptable ADMET properties. The results were compared with the wild type. These candidates could be envisioned as new hits against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 main protease mutation analysis via a kinematic method

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is the virus responsible for the COVID-19 pandemic. COVID-19 continues to cause millions of deaths globally in part due to immune-evading mutations. SARS-CoV-2 main protease (Mpro) is an important enzyme for viral replication and potentially an effective drug target. Mutations affect the dynamics of enzymes and thereby their activity and ability to bind ligands. Here, we use kinematic flexibility analysis (KFA) to identify how mutations and ligand binding changes the conformational flexibility of Mpro. KFA decomposes macromolecules into regions of different flexibility near-instantly from a static structure, allowing conformational dynamics analysis at scale. Altogether, we analyzed 47 mutation sites across 69 Mpro-ligand complexes resulting in more than 3300 different structures which includes 69 mutated structures with all 47 sites mutated simultaneously and 3243 single residue mutated structures. We found that mutations generally increased the conformational flexibility of the protein. Understanding the impact of mutations on the flexibility of Mpro is essential for identifying potential drug targets in the treatment of SARS-CoV-2. Further studies in this area can offer valuable insights into the mechanisms of molecular recognition.

Repurposing and computational design of PARP inhibitors as SARS-CoV-2 inhibitors

Coronavirus disease 2019 (COVID-19) is a recent pandemic that caused serious global emergency. To identify new and effective therapeutics, we employed a drug repurposing approach. The poly (ADP ribose) polymerase inhibitors were used for this purpose and were repurposed against the main protease (Mpro) target of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The results from these studies were used to design compounds using the 'Grow Scaffold' modules available on Discovery Studio v2018. The three designed compounds, olaparib 1826 and olaparib 1885, and rucaparib 184 demonstrated better CDOCKER docking scores for Mpro than their parent compounds. Moreover, the compounds adhered to Lipinski's rule of five and demonstrated a synthetic accessibility score of 3.55, 3.63, and 4.30 for olaparib 1826, olaparib 1885, and rucaparib 184, respectively. The short-range Coulombic and Lennard-Jones potentials also support the potential binding of the modified compounds to Mpro. Therefore, we propose these three compounds as novel SARS-CoV-2 inhibitors.

Unlocking the potential of approved drugs for the allosteric inhibition of tropomyosin-receptor kinase A using molecular docking and molecular dynamics studies

Tropomyosin-receptor kinase A (TrkA) is the primary isoform among the tropomyosin-receptor kinases that have been associated with human cancer development, contributing to approximately 7.4% of all cancer cases. TrkA represents an attractive target for cancer treatment; however, currently available TrkA inhibitors face limitations in terms of resistance development and potential toxicity. Hence, the objective of this study was to identify new allosteric-approved inhibitors of TrkA that can overcome these challenges and be employed in cancer therapy. To achieve this goal, a screening of 9,923 drugs from the ChEMBL database was conducted to assess their repurposing potential using molecular docking. The top 49 drug candidates, exhibiting the highest docking scores (-11.569 to -7.962 kcal/mol), underwent MM-GBSA calculations to evaluate their binding energies. Delanzomib and tibalosin, the top two drugs with docking scores of -10.643 and -10.184 kcal/mol, respectively, along with MM-GBSA dG bind values of -67.96 and -50.54 kcal/mol, were subjected to 200 ns molecular dynamic simulations, confirming their stable interactions with TrkA. Based on these findings, we recommend further experimental evaluation of delanzomib and tibalosin to determine their potential as allosteric inhibitors of TrkA. These drugs have the potential to provide more effective and less toxic therapeutic alternatives. The approach employed in this study, which involves repurposing drugs through molecular docking and molecular dynamics, serves as a valuable tool for identifying novel drug candidates with distinct therapeutic uses. This methodology can contribute to reducing the attrition rate and expediting the process of drug discovery.

Turning down PI3K/AKT/mTOR signalling pathway by natural products: an in silico multi-target approach

The PI3K/AKT/mTOR pathway is a significant target for cancer drug discovery. Many efforts have focused on discovering new inhibitors against key kinase proteins involved in this pathway for cancer treatment. PI3K/mTOR dual inhibitors, such as PKI-179, have been reported to be more effective than agents that act only on a single protein target. The present computational study aimed to discover triple target inhibitors against PI3K, AKT, and mTOR proteins. Accordingly, the PI3K protein bound with the ligand was used as input for e-pharmacophore modelling to generate the pharmacophore hypothesis and then screened for a library of 270,540 natural products from the Zinc database resulting in 57,220 compounds that matched the hypothesis. These compounds were then docked into the active site of PI3K, resulting in 292 compounds with better docking scores than the co-crystallized ligand. These compounds were re-docked into AKT and mTOR proteins. Besides, MM-GBSA binding free energy calculations, MD simulations, and ADMET prediction were carried out, leading to 5 potential triple-target inhibitors namely, ZINC000014644152, ZINC000014760695, ZINC000014644839, ZINC000095099451, and ZINC000005998557. In conclusion, these inhibitors may be possible leads for inhibiting PI3K/AKT/mTOR pathway, and they may be further evaluated in vitro and clinically as anticancer agents.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Insights from computational studies on the potential of natural compounds as inhibitors against SARS-CoV-2 spike omicron variant

Coronavirus disease 2019 (COVID-19) is a major global health emergency, with more than six million deaths worldwide. It is becoming increasingly challenging to treat COVID-19 due to the emergence of novel variants. The omicron variant is capable to evade defences and spread quickly. Among many validated COVID-19 targets, the spike (S) protein plays an important role in receptor recognition (via the S1 subunit) and membrane fusion (via the S2 subunit). The S protein is one of the vital targets for the development of drugs to combat this illness. In this research, we applied various computational methods such as molecular docking, molecular dynamics, MM-GBSA calculations, and ADMET prediction to identify potential natural products from Saudi medicinal plants against the spike omicron variant. As a result, three compounds (LTS0002490, LTS0117007, and LTS0217912) were identified with better binding affinity to the spike omicron variant compared to the reference compound (VE607). In addition, these compounds showed stable interactions with the target during molecular dynamics simulations for 140 ns. Last, these compounds have optimal ADMET properties. We suggest that these compounds may be considered promising hits to treat COVID-19 if experimentally validated.

Drug repurposing against main protease and RNA-dependent RNA polymerase of SARS-CoV-2 using molecular docking, MM-GBSA calculations and molecular dynamics

A virus called severe acute respiratory distress syndrome coronavirus type 2 (SARS‐CoV‐2) is the causing organism of coronavirus disease 2019 (COVID-19), which has severely affected human life and threatened public health. The pandemic took millions of lives worldwide and caused serious negative effects on human society and the economy. SARS-CoV-2 main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) are interesting targets due to their crucial role in viral replication and growth. Since there is only one approved therapy for COVID-19, drug repurposing is a promising approach to finding molecules with potential activity against COVID-19 in a short time and at minimal cost. In this study, virtual screening was performed on the ChEMBL library containing 9923 FDA-approved drugs, using various docking filters with different accuracy. The best drugs with the highest docking scores were further examined for molecular dynamics (MD) studies and MM-GBSA calculations. The results of this study suggest that nadide, cangrelor and denufosol are promising potential candidates against COVID-19. Further in vitro, preclinical and clinical studies of these candidates would help to discover safe and effective anti-COVID-19 drugs.