A computational study on active constituents of Habb-ul-aas and Tabasheer as inhibitors of SARS-CoV-2 main protease

Investigating the potential of natural compounds as novel inhibitors of SARS-CoV-2 RdRP using computational approaches

COVID-19 is a highly contagious disease caused by SARS-CoV-2. Currently, no vaccines or antiviral treatments are available to combat this deadly virus; however, precautions and some repurposed medicines are available to contain COVID-19. RNA-dependent RNA polymerase (RdRP) plays an important role in the replication or transcription of viral mechanisms. Approved antiviral drug such as Remdesivir has shown inhibitory activity against SARS-CoV-2 RdRP. The purpose of this study was to carry out a rational screening of natural products against SARS-CoV-2 RdRP, which may serve as a basis to develop a treatment option against COVID-19. For this purpose, a protein and structure conservation analysis of SARS-CoV-2 RdRP was performed to check mutations. A library of 15,000 phytochemicals was developed from literature review, ZINC database, PubChem and MPD3 database; and was used to performed molecular docking and molecular dynamics simulation (MD) analysis. The top-ranked compounds were subjected to pharmacokinetic and pharmacological studies. Among them, top 7 compounds (Spinasaponin A, Monotropane, Neohesperidoe, Posin, Docetaxel, Psychosaponin B2, Daphnodrine M, and Target Remedesvir) were noticed to interact with the active site residues. MD simulation in aqueous solution suggested conformational flexibility of loop regions in the complex to stabilize the docked inhibitors. Our study revealed that the studied compounds have potential to bind to the active site residues of SARS-CoV-2 RdRP. Although, this computational work is not experimentally determined but the structural information and selected compounds might help in the design of antiviral drugs targeting SAR-CoV-2 by inhibiting the activity of SARS-CoV-2 RdRP.

Structure-guided identification of potent inhibitors of ROS1 kinase for therapeutic development against non-small cell lung cancer

Proto-oncogene tyrosine-protein kinase ROS (ROS1) is a member of the sevenless receptor, which affects epithelial cell differentiation and is highly expressed in a variety of tumor cells. The elevated expression and dysfunction of ROS1 have been involved in various malignancies, such as non-small cell lung cancer (NSCLC), stomach cancer, ovarian, breast cancer, cholangiocarcinoma, colorectal cancer, adenosarcoma, oesophageal cancer, etc. ROS1 has been postulated as a potential drug target in anticancer therapeutics. In this study, we carried out a virtual screening of phytochemicals against ROS1 to identify its potential inhibitors. The virtual screening process was performed on the ROS1 structure, where two phytochemicals, Helioscopinolide C and Taiwanin C, were identified. These compounds resulted from filters like Lipinski rule of five, PAINS filter, binding affinities values, and all-atom molecular dynamics (MD) simulations followed by principal component analysis (PCA) and essential dynamics. The findings of this study highlight the role of ROS1 in multiple physiological candidates and its therapeutic targeting using phytochemicals. This study suggests Helioscopinolide C and Taiwanin C as potential compounds for therapeutic development targeting ROS1-associated non-small cell lung cancer for clinical applications. Further in vitro and in vivo experiments are required to validate these findings.Communicated by Ramaswamy H. Sarma.

Integrating network pharmacology with molecular docking to rationalize the ethnomedicinal use of Alchornea laxiflora (Benth.) Pax & K. Hoffm. for efficient treatment of depression

Background: Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) has been indicated in traditional medicine to treat depression. However, scientific rationalization is still lacking. Hence, this study aimed to investigate the antidepressant potential of A. laxiflora using network pharmacology and molecular docking analysis. Materials and methods: The active compounds and potential targets of A. laxiflora and depression-related targets were retrieved from public databases, such as PubMed, PubChem, DisGeNET, GeneCards, OMIM, SwissTargetprediction, BindingDB, STRING, and DAVID. Essential bioactive compounds, potential targets, and signaling pathways were predicted using in silico analysis, including BA-TAR, PPI, BA-TAR-PATH network construction, and GO and KEGG pathway enrichment analysis. Later on, with molecular docking analysis, the interaction of essential bioactive compounds of A. laxiflora and predicted core targets of depression were verified. Results: The network pharmacology approach identified 15 active compounds, a total of 219 compound-related targets, and 14,574 depression-related targets with 200 intersecting targets between them. SRC, EGFR, PIK3R1, AKT1, and MAPK1 were the core targets, whereas 3-acetyloleanolic acid and 3-acetylursolic acid were the most active compounds of A. laxiflora with anti-depressant potential. GO functional enrichment analysis revealed 129 GO terms, including 82 biological processes, 14 cellular components, and 34 molecular function terms. KEGG pathway enrichment analysis yielded significantly enriched 108 signaling pathways. Out of them, PI3K-Akt and MAPK signaling pathways might have a key role in treating depression. Molecular docking analysis results exhibited that core targets of depression, such as SRC, EGFR, PIK3R1, AKT1, and MAPK1, bind stably with the analyzed bioactive compounds of A. laxiflora. Conclusion: The present study elucidates the bioactive compounds, potential targets, and pertinent mechanism of action of A. laxiflora in treating depression. A. laxiflora might exert an antidepressant effect by regulating PI3K-Akt and MAPK signaling pathways. However, further investigations are required to validate.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Baicalin Targets HSP70/90 to Regulate PKR/PI3K/AKT/eNOS Signaling Pathways

Baicalin is a major active ingredient of traditional Chinese medicine Scutellaria baicalensis, and has been shown to have antiviral, anti-inflammatory, and antitumor activities. However, the protein targets of baicalin have remained unclear. Herein, a chemical proteomics strategy was developed by combining baicalin-functionalized magnetic nanoparticles (BCL-N3@MNPs) and quantitative mass spectrometry to identify the target proteins of baicalin. Bioinformatics analysis with the use of Gene Ontology, STRING and Ingenuity Pathway Analysis, was performed to annotate the biological functions and the associated signaling pathways of the baicalin targeting proteins. Fourteen proteins in human embryonic kidney cells were identified to interact with baicalin with various binding affinities. Bioinformatics analysis revealed these proteins are mainly ATP-binding and/or ATPase activity proteins, such as CKB, HSP86, HSP70-1, HSP90, ATPSF1β and ACTG1, and highly associated with the regulation of the role of PKR in interferon induction and the antiviral response signaling pathway (P = 10-6), PI3K/AKT signaling pathway (P = 10-5) and eNOS signaling pathway (P = 10-4). The results show that baicalin exerts multiply pharmacological functions, such as antiviral, anti-inflammatory, antitumor, and antioxidant functions, through regulating the PKR and PI3K/AKT/eNOS signaling pathways by targeting ATP-binding and ATPase activity proteins. These findings provide a fundamental insight into further studies on the mechanism of action of baicalin.