Pharmacophore-Model-Based Drug Repurposing for the Identification of the Potential Inhibitors Targeting the Allosteric Site in Dengue Virus NS5 RNA-Dependent RNA Polymerase

Allosteric inhibition of dengue virus RNA-dependent RNA polymerase by Litsea cubeba phytochemicals: a computational study

RNA-dependent RNA polymerase (RdRp) is considered a potential drug target for dengue virus (DENV) inhibition and has attracted attention in antiviral drug discovery. Here, we screened 121 natural compounds from Litsea cubeba against DENV RdRp using various approaches of computer-based drug discovery. Notably, we identified four potential compounds (Ushinsunine, Cassameridine, (+)-Epiexcelsin, (-)-Phanostenine) with good binding scores and allosteric interactions with the target protein. Moreover, molecular dynamics simulation studies were done to check the conformational stability of the complexes under given conditions. Additionally, we performed post-simulation analysis to find the stability of potential drugs in the target protein. The findings suggest Litsea cubeba-derived phytomolecules as a therapeutic solution to control DENV infection.Communicated by Ramaswamy H. Sarma.

Computational biology approaches for drug repurposing

The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.

Antiviral actions of natural compounds against dengue virus RNA dependent RNA polymerase: insights from molecular dynamics and Gibbs free energy landscape

Dengue fever, a major global health challenge, affects nearly half the world's population and lacks effective treatments or vaccines. Addressing this, our study focused on natural compounds that potentially inhibit the dengue virus's RNA-dependent RNA polymerase (RdRp), a crucial target in the viral replication cycle. Utilizing the MTiOpenScreen webserver, we screened 1226 natural compounds from the NP-lib database. This screening identified four promising compounds ZINC000059779788, ZINC0000044404209, ZINC0000253504517 and ZINC0000253499146), each demonstrating high negative binding energies between -10.4 and -9.9 kcal/mol, indicative of strong potential as RdRp inhibitors. These compounds underwent rigorous validation through re-docking and a detailed 100 ns molecular dynamics (MD) simulation. This analysis affirmed the dynamic stability of the protein-ligand complexes, a critical factor in the effectiveness of potential drug candidates. Additionally, we conducted essential dynamics and free energy landscape calculations to understand the structural transitions in the RdRp protein upon ligand binding, providing valuable insights into the mechanism of inhibition. Our findings present these natural molecules as promising therapeutic agents against the dengue virus. By targeting the allosteric site of RdRp, these compounds offer a novel approach to hinder the viral replication process. This research significantly contributes to the search for effective anti-dengue treatments, positioning natural compounds as potential key players in dengue virus control strategies.

Computational Advancement towards the Identification of Natural Inhibitors for Dengue Virus: A Brief Review

Viral infectious illnesses represent a severe hazard to human health due to their widespread incidence worldwide. Among these ailments, the dengue virus (DENV) infection stands out. World Health Organization (WHO) estimates that DENV infection affects ~400 million people each year, with potentially fatal symptoms showing up in 1% of the cases. In several instances, academic and pharmaceutical researchers have conducted several pilot and clinical studies on a variety of topics, including viral epidemiology, structure and function analyses, infection source and route, therapeutic targets, vaccinations, and therapeutic drugs. Amongst Takeda, TAK-003, Sanofi, Dengvaxia®, and Butantan/NIH/Merck, Dengvaxia® (CYD-TDV) is the only licensed vaccination yet; however, the potential inhibitors are under development. The biology and evolution of DENVs are briefly discussed in this review, which also compiles the most recent studies on prospective antiviral targets and antiviral candidates. In conclusion, the triumphs and failures have influenced the development of anti-DENV medications, and the findings in this review article will stimulate more investigation.

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus.

Assessing the inhibitory potential of anti-dengue compounds against Japanese encephalitis virus RNA dependent RNA polymerase: an in silico study

Japanese encephalitis (JE), a neurological infection of severe nature, is caused by the Japanese encephalitis virus (JEV) and is transmitted by the mosquito vector. The polymerase domain of Non-structural 5 (NS5), which is also referred to as RdRp (RNA-dependent RNA polymerase), is considered a potential therapeutic target for JEV. The present study employed molecular dynamics modelling and high-throughput virtual screening to evaluate the possible antiviral activity of anti-dengue drugs against JEV RdRp. Furthermore, a ranking was performed utilising the MM/GBSA analysis to identify the three most promising compounds. Compound ID 57409246 exhibited the highest binding affinity with the protein, as evidenced by its minimum binding free energy of -72.96 kcal/mole. In contrast, the other two compounds had minimum binding free energies of -67.57 and -59.19 kcal/mole, respectively. Upon conducting a 100 nanosecond molecular dynamics simulation to confirm the binding of the chemical complexes, it was observed that the three hits, namely 57409246, 70683874, and 44577154, exhibited a consistent and stable RMSD. Subsequently, the binding strength of the trajectory was confirmed through MM/GBSA analysis. The compounds 70683874 and 57409246 exhibited the lowest binding free energies, which were -97.58 kcal/mol and -96.38 kcal/mol, respectively. The binding free energy (ΔG Bind) values for the native ligand ATP and molecule 44577154 were -65.64 kcal/mol and -69.44 kcal/mol, respectively. Overall, compared to the native ligand ATP, all three compounds exhibited higher binding affinity. The study proposes three anti-dengue molecules as a potential remedy for JE, which can be confirmed through in vitro and in vivo investigations.Communicated by Ramaswamy H. Sarma.

Antiviral Activity of an Indole-Type Compound Derived from Natural Products, Identified by Virtual Screening by Interaction on Dengue Virus NS5 Protein

Dengue is an acute febrile illness caused by the Dengue virus (DENV), with a high number of cases worldwide. There is no available treatment that directly affects the virus or the viral cycle. The objective of this study was to identify a compound derived from natural products that interacts with the NS5 protein of the dengue virus through virtual screening and evaluate its in vitro antiviral effect on DENV-2. Molecular docking was performed on NS5 using AutoDock Vina software, and compounds with physicochemical and pharmacological properties of interest were selected. The preliminary antiviral effect was evaluated by the expression of the NS1 protein. The effect on viral genome replication and/or translation was determined by NS5 production using DENV-2 Huh-7 replicon through ELISA and viral RNA quantification using RT-qPCR. The in silico strategy proved effective in finding a compound (M78) with an indole-like structure and with an effect on the replication cycle of DENV-2. Treatment at 50 µM reduced the expression of the NS5 protein by 70% and decreased viral RNA by 1.7 times. M78 is involved in the replication and/or translation of the viral genome.

Exploration of potent antiviral phytomedicines from Lauraceae family plants against SARS-CoV-2 RNA-dependent RNA polymerase

RNA-dependent RNA polymerase, also known as RdRp, is a possible therapeutic target that could be used to suppress the proliferation of RNA viruses such as SARS-CoV-2. This protein has two major functional sites (a) catalytic and (b) substrate entry, which regulate the natural substrate entry and its corresponding interaction with the protein. In this study, a computational drug design pipeline was applied to investigate potential inhibitors against SARS-CoV-2 RdRp from Lauraceae plants, and five top hits were selected based on the docked score (< -7 kcal/mol). The docking study suggested that the Glochidioboside had a minimum binding score of -7.8 kcal/mol. This compound showed total five hydrogen bonds while two of them were with catalytic residues Asp618 and Asp760. However, another compound, Sitogluside showed a binding score of -7.3 kcal/mol with four hydrogen bonds targeting three functional residues (Arg555, Ser759, and Asp760). Later, 100 ns explicit solvent molecular dynamics (MD) simulation was performed to evaluate the stability of the protein-ligand docked system. These compounds translocated their positions from the catalytic site to the substrate entry site, as observed in the MD simulation trajectory. However, translocation did not affect the binding strength of these compounds, and they retained the strong binding affinity (ΔG < -11.5 kcal/mol), estimated using the MM/GBSA method. In general, the findings of this study indicated the potential therapeutic compounds that may be used targeting SARS-CoV-2 RdRp. However, these compounds still need to be validated by experimentation in order to determine their inhibitory function.Communicated by Ramaswamy H. Sarma.