The recent pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) calls the whole world into a medical emergency. For tackling Coronavirus Disease 2019 , researchers from around the world are swiftly working on designing and identifying inhibitors against all possible viral key protein targets. One of the attractive drug targets is guanine-N7 methyltransferase which plays the main role in capping the 5 0 -ends of viral genomic RNA and sub genomic RNAs, to escape the host's innate immunity. We performed homology modeling and molecular dynamic (MD) simulation, in order to understand the molecular architecture of Guanosine-P3-Adenosine-5',5'-Triphosphate (G3A) binding with C-terminal N7-MTase domain of nsp14 from SARS-CoV-2. The residue Asn388 is highly conserved in present both in N7-MTase from SARS-CoV and SARS-CoV-2 and displays a unique function in G3A binding. For an in-depth understanding of these substrate specificities, we tried to screen and identify inhibitors from the Traditional Chinese Medicine (TCM) database. The combination of several computational approaches, including screening, MM/GBSA, MD simulations, and PCA calculations, provides the screened compounds that readily interact with the G3A binding site of homology modeled N7-MTase domain. Compounds from this screening will have strong potency towards inhibiting the substrate-binding and efficiently hinder the viral 5'-end RNA capping mechanism. We strongly believe the final compounds can become COVID-19 therapeutics, with huge international support.The focus of this study is to screen for antiviral inhibitors blocking guanine-N7 methyltransferase (N7-MTase), one of the key drug targets involved in the first methylation step of the SARS-CoV-2 RNA capping mechanism. Compounds binding the substrate-binding site can interfere with enzyme catalysis and impede 5'-end cap formation, which is crucial to mimic host RNA and evade host cellular immune responses. Therefore, our study proposes the top hit compounds from the Traditional Chinese Medicine (TCM) database using a combination of several computational approaches.

The recent pandemic outbreak of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), raised global health and economic concerns. Phylogenetically, SARS-CoV-2 is closely related to SARS-CoV, and both encode the enzyme main protease (Mpro/3CLpro), which can be a potential target inhibiting viral replication. Through this work, we have compiled the structural aspects of Mpro conformational changes, with molecular modeling and 1-μs MD simulations. Long-scale MD simulation resolves the mechanism role of crucial amino acids involved in protein stability, followed by ensemble docking which provides potential compounds from the Traditional Chinese Medicine (TCM) database. These lead compounds directly interact with active site residues (His41, Gly143, and Cys145) of Mpro, which plays a crucial role in the enzymatic activity. Through the binding mode analysis in the S1, S1′, S2, and S4 binding subsites, screened compounds may be functional for the distortion of the oxyanion hole in the reaction mechanism, and it may lead to the inhibition of Mpro in SARS-CoV-2. The hit compounds are naturally occurring compounds; they provide a sustainable and readily available option for medical treatment in humans infected by SARS-CoV-2. Henceforth, extensive analysis through molecular modeling approaches explained that the proposed molecules might be promising SARS-CoV-2 inhibitors for the inhibition of COVID-19, subjected to experimental validation.

HIV-1 integrase is a unique promising component of the viral replication cycle, catalyzing the integration of reverse transcribed viral cDNA into the host cell genome. Generally, IN activity requires both viral as well as a cellular co-factor in the processing replication cycle. Among them, the human lens epithelium-derived growth factor (LEDGF/p75) represented as promising cellular co-factor which supports the viral replication by tethering IN to the chromatin. Due to its major importance in the early steps of HIV replication, the interaction between IN and LEDGF/p75 has become a pleasing target for anti-HIV drug discovery. The present study involves the finding of novel inhibitor based on the information of dimeric CCD of IN in complex with known inhibitor, which were carried out by applying a structure-based virtual screening concept with molecular docking. Additionally, Free binding energy, ADME properties, PAINS analysis, Density Functional Theory, and Enrichment Calculations were performed on selected compounds for getting a best lead molecule. On the basis of these analyses, the current study proposes top 3 compounds: Enamine-Z742267384, Maybridge-HTS02400, and Specs-AE-848/37125099 with acceptable pharmacological properties and enhanced binding affinity to inhibit the interaction between IN and LEDGF/p75. Furthermore, Simulation studies were carried out on these molecules to expose their dynamics behavior and stability. We expect that the findings obtained here could be future therapeutic agents and may provide an outline for the experimental studies to stimulate the innovative strategy for research community.

Since the beginning of the century, viral infection has become a widespread problem to public health globally. Recently, the latest newcomer of the Flaviviridae family, the Zika virus, has emerged as a potential global threat to human health. Due to lack of sufficient data at present, Zika infection has become a major cause of Zika fever, central nervous system malformations such as microcephaly, severe neuroimmunopathology, fetal abnormalities, and recently Guillain-Barre syndrome. The lack of genomic as well as proteomic information keeps the Zika virus under examination by a multitude of researchers. The rapid increase in Zika viral infections has been classified as a public health emergency by the World Health Organization. Neither preventive antiviral drugs nor effective vaccines are available on the market for combating Zika infection. Urgent innovative research is necessary to facilitate the development of protective and fruitful therapeutic agents, to improve lifespan of individuals throughout the world. Thus, we present this review to summarize the current research findings for Zika viral infection and to highlight the importance of computational drug discovery in the development of potent antiviral inhibitors against the Zika virus. We also anticipate that the information in this review will present a valuable opportunity for the prediction of efficient novel therapeutic remedies for controlling Zika.

Background: The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases has overwhelmed health and public health services. AI and ML algorithms have extended its major role in tracking the disease patterns, and in identifying possible treatment of disease. Objective: To identify potential COVID-19 protease inhibitor through shape-based Machine Learning assisted by Molecular docking and Molecular Dynamics simulation. Methods: 31 repurposed compounds have been selected targeting coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from 3D shape to pharmacophoric features of its seed compound. Ligand-Receptor docking was performed with Optimized Potential for Liquid Simulations (OPLS3) algorithms to identify high-affinity compounds from the list of selected candidates for 6LU7. This compound was subjected to Molecular Dynamic Simulations followed by ADMET studies and other analysis. Results: Shape-based Machine learning reported Remdesivir, Valrubicin, Aprepitant, and Fulvestrant and a novel therapeutic compound as the best therapeutic agents with the highest affinity for its target protein. Among the best shape-based compounds, the novel theoretical compound was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorvEMBS'. Further, toxicity analysis showed nCorv-EMBS to be efficacious that can be qualified as a 6LU7 inhibitor in COVID-19. Conclusion: An effective ACE-II, GAK, AAK1, and protease 3C blockers that can serve a novel therapeutic approach to block the binding and attachment of COVID-19 protein (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 Lung cells. The novel theoretical compound nCorv-EMBS herein proposed stands as a promising inhibitor that can be extended for entering phases of clinical trials for COVID-19 treatment.