Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2

The ongoing COVID-19 outbreak have posed a significant threat to public health worldwide. Recently Toll-like receptor (TLR) has been proposed to be the drug target of SARS-CoV-2 treatment, the specificity and efficacy of such treatments remain unknown. In the present study we performed the investigation of repurposed drugs via a framework comprising of Search Tool for Interacting Chemicals (STITCH), Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking, and virus-host-drug interactome mapping. Chloroquine (CQ) and hydroxychloroquine (HCQ) were utilized as probes to explore the interaction network that is linked to SARS-CoV-2. 47 drug targets were shown to be overlapped with SARS-CoV-2 network and were enriched in TLR signaling pathway. Molecular docking analysis and molecular dynamics simulation determined the direct binding affinity of TLR9 to CQ and HCQ. Furthermore, we established SARS-CoV-2-human-drug protein interaction map and identified the axis of TLR9-ERC1-Nsp13 and TLR9-RIPK1-Nsp12. Therefore, the elucidation of the interactions of SARS-CoV-2 with TLR9 axis will not only provide pivotal insights into SARS-CoV-2 infection and pathogenesis but also improve the treatment against COVID-19.

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The Potential role of Procyanidin as a Therapeutic Agent against SARS-CoV-2: A Text Mining, Molecular Docking and Molecular Dynamics Simulation Approach

10.26434/chemrxiv.12579599 ◽

2020 ◽

Author(s):

Nikhil Maroli ◽

Balu Bhasuran ◽

Jeyakumar Natarajan ◽

Ponmalai Kolandaivel

Keyword(s):

Molecular Dynamics ◽

Molecular Docking ◽

Molecular Dynamics Simulation ◽

Text Mining ◽

Van Der Waals ◽

Interaction Network ◽

Van Der Waals Interactions ◽

Entity Recognition ◽

Dynamics Simulation ◽

Inhibition Mechanism

A novel coronavirus (SARS-CoV-2) has caused a major outbreak in human all over the world. There are several proteins interplay during the entry and replication of this virus in human. Here, we have used text mining and named entity recognition method to identify co-occurrence of the important COVID 19 genes/proteins in the interaction network based on the frequency of the interaction. Network analysis revealed a set of genes/proteins, highly dense genes/protein clusters and sub-networks of Angiotensin-converting enzyme 2 (ACE2), Helicase, spike (S) protein (trimeric), membrane (M) protein, envelop (E) protein, and the nucleocapsid (N) protein. The isolated proteins are screened against procyanidin-a flavonoid from plants using molecular docking. Further, molecular dynamics simulation of critical proteins such as ACE2, Mpro and spike proteins are performed to elucidate the inhibition mechanism. The strong network of hydrogen bonds and hydrophobic interactions along with van der Waals interactions inhibit receptors, which are essential to the entry and replication of the SARS-CoV-2. The binding energy which largely arises from van der Waals interactions is calculated (ACE2=-50.21 ± 6.3, Mpro=-89.50 ± 6.32 and spike=-23.06 ± 4.39) through molecular mechanics Poisson-Boltzmann surface area also confirm the affinity of procyanidin towards the critical receptors.

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Virtual Screening Technique Used to Estimate the Mechanism of Adhatoda vasica Nees for the Treatment of Rheumatoid Arthritis Based on Network Pharmacology and Molecular Docking

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/5872980 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Wenxiang Wang ◽

Yunsen Zhang ◽

Jie Luo ◽

Rushan Wang ◽

Ce Tang ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Molecular Docking ◽

Drug Targets ◽

Molecular Mechanisms ◽

Experimental Studies ◽

Interaction Network ◽

Network Pharmacology ◽

Chemical Components ◽

Chemical Compositions ◽

Adhatoda Vasica

Adhatoda vasica Nees (AVN) is commonly used to treat joint diseases such as rheumatoid arthritis (RA) in ethnic minority areas of China, especially in Tibetan and Dai areas, and its molecular mechanisms on RA still remain unclear. Network pharmacology, a novel strategy, utilizes bioinformatics to predict and evaluate drug targets and interactions in disease. Here, network pharmacology was used to investigate the mechanism by which AVN acts in RA. The chemical compositions and functional targets of AVN were retrieved using the systematic pharmacological analysis platform PharmMapper. The targets of RA were queried through the DrugBank database. The protein-protein interaction network (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets were constructed in the STRING database, and the network visualization analysis was performed in Cytoscape. Maestro 11.1, a type of professional software, was used for verifying prediction and analysis based on network pharmacology. By comparing the predicted target information with the targets of RA-related drugs, 25 potential targets may be related to the treatment of RA, among which MAPK1, TNF, DHODH, IL2, PTGS2, and JAK2 may be the main potential targets for the treatment of RA. Finally, the chemical components and potential target proteins were scored by molecular docking, and compared with the ligands of the protein, the prediction results of network pharmacology were preliminarily verified. The active ingredients and mechanism of AVN against RA were firstly investigated using network pharmacology. Additionally, this research provided a solid foundation for further experimental studies.

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Molecular Docking Reveals Ivermectin and Remdesivir as Potential Repurposed Drugs Against SARS-CoV-2

Frontiers in Microbiology ◽

10.3389/fmicb.2020.592908 ◽

2021 ◽

Vol 11 ◽

Author(s):

Ahmad F. Eweas ◽

Amr A. Alhossary ◽

Ahmed S. Abdel-Moneim

Keyword(s):

Molecular Docking ◽

Antiviral Agents ◽

Dynamics Simulation ◽

Cell Surface Receptor ◽

Treatment Protocols ◽

Global Pandemic ◽

Viral Proteases ◽

Surface Receptor ◽

Repurposed Drugs ◽

Post Entry

SARS-CoV-2 is a newly emerged coronavirus that causes a respiratory disease with variable severity and fatal consequences. It was first reported in Wuhan and subsequently caused a global pandemic. The viral spike protein binds with the ACE-2 cell surface receptor for entry, while TMPRSS2 triggers its membrane fusion. In addition, RNA dependent RNA polymerase (RdRp), 3′–5′ exoribonuclease (nsp14), viral proteases, N, and M proteins are important in different stages of viral replication. Accordingly, they are attractive targets for different antiviral therapeutic agents. Although many antiviral agents have been used in different clinical trials and included in different treatment protocols, the mode of action against SARS-CoV-2 is still not fully understood. Different potential repurposed drugs, including, chloroquine, hydroxychloroquine, ivermectin, remdesivir, and favipiravir, were screened in the present study. Molecular docking of these drugs with different SARS-CoV-2 target proteins, including spike and membrane proteins, RdRp, nucleoproteins, viral proteases, and nsp14, was performed. Moreover, the binding affinities of the human ACE-2 receptor and TMPRSS2 to the different drugs were evaluated. Molecular dynamics simulation and MM-PBSA calculation were also conducted. Ivermectin and remdesivir were found to be the most promising drugs. Our results suggest that both these drugs utilize different mechanisms at the entry and post-entry stages and could be considered potential inhibitors of SARS-CoV-2 replication.

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In silico tracking of SARS-CoV-2 Nsp1 structural variants in helix-turn-helix motif

10.22541/au.161659301.15541515/v1 ◽

2021 ◽

Author(s):

Shokouh Rezaei ◽

Filipe Pereira ◽

yahya sefidbakht

Keyword(s):

Conformational Changes ◽

Mrna Translation ◽

Dynamics Simulation ◽

Translation Efficiency ◽

Structural Protein ◽

Compact Structure ◽

Helix Loop Helix ◽

Host Virus Interactions ◽

And Function ◽

Virus Interactions

SARS-CoV-2 non-structural protein 1 (Nsp1) is a virulence factor that inhibits the innate immune response and translation of host mRNAs. Despite the relevance of Nsp1, few studies have been conducted to understand the effect of mutations on Nsp1 structure and function. Here, we provide a molecular dynamics simulation of SARS-CoV-2 and SARS-CoV-1 Nsp1 conformational changes. Our data supports the idea that SARS-CoV-2 Nsp1 has a less compact structure than SARS-CoV-1 Nsp1. Moreover, several mutations in the helix-loop-helix motif of Nsp1 C-terminal that may affect the interactions of the Nsp1-ribosome complex were investigated. Disordered regions in Nsp1 probably affect host-virus interactions, cross-species transmission, and virus-host range. Overall, these findings reinforce the importance of studying Nsp1 conformational changes in new variants and its effect on virulence of SARS-CoV-2, by altering inhibition potency of host mRNA translation efficiency.

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Molecular Docking and Dynamics Simulation of Natural Phenolic Compounds with GSK-3β: A Putative Target to Combat Mortality in Patients with COVID-19

Recent Patents on Inflammation & Allergy Drug Discovery ◽

10.2174/1872213x14666210916161447 ◽

2021 ◽

Vol 14 ◽

Author(s):

Zahra Khamverdi ◽

Zeinab Mohamadi ◽

Amir Taherkhani

Keyword(s):

Amino Acids ◽

Molecular Docking ◽

Phenolic Compounds ◽

Binding Affinity ◽

Active Sites ◽

Interaction Network ◽

Md Simulations ◽

Dynamics Simulation ◽

Discovery Studio ◽

Gsk 3Β

Objective: In this study, molecular docking analysis was performed to evaluate the binding affinity of 52 plant-based phenolics with the GSK-3β active sites. Moreover, Molecular Dynamics (MD) simulation was conducted to investigate the stability of interactions between the topranked phenolics and residues within the GSK-3β active sites. Methods: Molecular docking and MD simulations were performed using AutoDock and Discovery Studio Client software, respectively. Thereafter, pharmacokinetic and toxicological properties of top inhibitors were predicted using bioinformatics web tools. This study aimed to identify the most effective amino acids involved in the inhibition of GSK-3β based on the most stabilizing interactions between the residues and compounds, and also by considering the degree centrality in the ligand-amino acid interaction network for GSK-3β. Results: It was observed that procyanidin and amentoflavone could bind to the GSK-3β active sites at the picomolar (pM) scale as well as the binding affinity of ∆G binding < -13 kcal/mol, while the inhibition constant for theaflavin 3’-gallate, procyanidin B4, and rutin was calculated at the nanomolar (nM) scale, suggesting that these phenolic compounds can be considered as potential effective GSK-3β inhibitors. Furthermore, Val70, Ala83, Val135, and Tyr134 were found to be the most important amino acids involved in the inhibition of GSK-3β. Conclusion: The results of the current study may be useful in the prevention of several human disorders, including COVID-19, cancers, Alzheimer’s disease, diabetes mellitus, and cardiovascular diseases. However, wet-lab experiments need to be performed in the future.

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Discussion on the Mechanism of Lithospermum erythrorhizon in the Treatment of Cervical Cancer Based on Network Pharmacology and Molecular Docking Technology

Tobacco Regulatory Science ◽

10.18001/trs.7.5.1.165 ◽

2021 ◽

Vol 7 (5) ◽

pp. 3927-3933

Author(s):

Qiong Yan ◽

Fangwu Ye

Keyword(s):

Cervical Cancer ◽

Molecular Docking ◽

Drug Targets ◽

Target Genes ◽

Interaction Network ◽

Network Pharmacology ◽

Drug Candidate ◽

Pathway Enrichment Analysis ◽

Active Ingredients ◽

Stable Conformation

Objective To explore the “multi-component, multi-target, multi-pathway” mechanism of Lithospermum erythrorhizonagairtst cervical cancer. Methods The active ingredients and corresponding targets were screened through TCMSP, PubChem and SwissTargetPrediction databases. The GeneCarts platform was used to collect cervical cancer-related genes, and the intersection of drug targets and cervical cancer targets was analyzed. Use STRING to analyze protein interaction network, use Cytoscape software to construct component-target and core target interaction network, perform KEGG pathway enrichment analysis on core target genes, and conduct molecular docking verification.Results After screening, 12 main active ingredients of comfrey (including Shikonin A, 1-methoxyacetylshikonin, Shikonin B, etc.) and 35 key targets related to comfrey and cervical cancer were obtained (including ESR1, SRC, MMP9, PTGS2, etc.). And these genes were mainly enriched in 39 signaling pathways such as PI3K-Akt and estrogen. Molecular docking reminder that Lithospermum A has a higher affinity with ESR1, and Lithospermum B can form a stable conformation with SRC, MMP9, and PTGS2. Conclusion Lithospermum erythrorhizon is a potential drug candidate for the treatment of cervical cancer. It can treat cervical cancer through multi-component, multi-target, and multi-channel action.

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Molecular docking and dynamics simulation study of bioactive compounds from Ficus carica L. with important anticancer drug targets

PLoS ONE ◽

10.1371/journal.pone.0254035 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254035

Author(s):

Arun Bahadur Gurung ◽

Mohammad Ajmal Ali ◽

Joongku Lee ◽

Mohammad Abul Farah ◽

Khalid Mashay Al-Anazi

Keyword(s):

Molecular Docking ◽

Molecular Mechanics ◽

Surface Area ◽

Binding Affinity ◽

Bioactive Compounds ◽

Anticancer Drug ◽

Drug Targets ◽

Ficus Carica ◽

Dynamics Simulation ◽

Cyclin Dependent Kinase

Ficus carica L., commonly known as fig, has been used in traditional medicine for metabolic disorders, cardiovascular diseases, respiratory diseases and cancer. Various bioactive compounds have been previously isolated from the leaves, fruit, and bark, which have different pharmacological properties, but the anticancer mechanisms of this plant are not known. In the current study we focused on understanding the probable mechanisms underlying the anticancer activity of F. carica plant extracts by molecular docking and dynamic simulation approaches. We evaluated the drug-likeness of the active constituents of the plant and explored its binding affinity with selected anticancer drug target receptors such as cyclin-dependent kinase 2 (CDK-2), cyclin-dependent kinase 6 (CDK-6), topoisomerase-I (Topo I), topoisomerase-II (Topo II), B-cell lymphoma 2 (Bcl-2), and vascular endothelial growth factor receptor 2 (VEGFR-2). In silico toxicity studies revealed that thirteen molecules out of sixty-eight major active compounds in the plant extract have acceptable drug-like properties. Compound 37 (β-bourbonene) has a good binding affinity with the majority of drug targets, as revealed by molecular docking studies. The complexes of the lead molecules with the drug receptors were stable in terms of molecular dynamics simulation derived parameters such as root mean square deviation and radius of gyration. The top ten residues contributing significantly to the binding free energies were deciphered through analysis of molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA). Thus, the results of our studies unravel the potential of F. carica bioactive compounds as anticancer candidate molecules against selected macromolecular receptors.

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The Potential role of Procyanidin as a Therapeutic Agent against SARS-CoV-2: A Text Mining, Molecular Docking and Molecular Dynamics Simulation Approach

10.26434/chemrxiv.12579599.v1 ◽

2020 ◽

Author(s):

Nikhil Maroli ◽

Balu Bhasuran ◽

Jeyakumar Natarajan ◽

Ponmalai Kolandaivel

Keyword(s):

Molecular Dynamics ◽

Molecular Docking ◽

Molecular Dynamics Simulation ◽

Text Mining ◽

Hydrophobic Interactions ◽

Interaction Network ◽

Van Der Waals Interactions ◽

Entity Recognition ◽

Dynamics Simulation ◽

Inhibition Mechanism

A novel coronavirus (SARS-CoV-2) has caused a major outbreak in the human all over the world. There are several proteins interplay during the entry and replication of this virus in human. Here, we have used text mining andnamed entity recognition method to identify co-occurrence of the important COVID 19 genes/proteins in the interaction network based on the frequency of the interaction. Network analysis revealed a set of genes/proteins, highly dense genes/protein clusters and sub networks of Angiotensin converting enzyme 2 (ACE2), Helicase, spike (S) protein (trimeric), membrane (M) protein, envelop (E) protein, and the nucleocapsid (N) protein. The isolated proteins are screened against procyanidin-a flavonoid from plants using molecular docking. Further, molecular dynamics simulation of critical proteins such as ACE2, Mpro and spike proteins are performed to elucidate the inhibition mechanism. The strong network of hydrogen bonds and hydrophobic interactions along with van der Waals interactions inhibit receptors, which are essential to the entry and replication of the SARS-CoV-2. The binding energy calculated (ACE2=-50.21 ± 6.3, Mpro=-89.50 ± 6.32 and spike=-23.06 ± 4.39) through molecular mechanics poisson−boltzmann surface area also confirm the affinity of procyanidin towards the critical receptors.

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EvoProDom: Evolutionary model of protein families by means of translocations of protein domains

10.1101/2020.02.23.961532 ◽

2020 ◽

Author(s):

Gon Carmi ◽

Alessandro Gorohovski ◽

Milana Frenkel-Morgenstern

Keyword(s):

Protein Design ◽

Protein Domains ◽

Evolutionary Model ◽

Protein Domain ◽

Protein Families ◽

Orthologous Protein ◽

Search Tool ◽

Host Virus Interactions ◽

Virus Interactions ◽

Evolution Of Proteins

AbstractHere, we developed a novel evolution of protein domains (EvoProDom) model for evolution of proteins, which was based on mix and merge of protein domains. We collected and integrated genomic and proteome data for 109 organisms. These data include protein domain content and orthologous protein families. In EvoProDom, we defined evolutionary events, such as translocations, as reciprocal exchanges of protein domains between orthologous proteins of different organisms. We found that protein domains, which frequently appear in translocation events, were enriched in trans-splicing events, i.e., producing novel transcripts fused from two distinct genes. We presented in EvoProDom, a general method to obtain protein domain content and orthologous protein annotation, by predicting these data from protein sequences using the Pfam search tool and KoFamKOALA, respectively. This method can be implemented in other research such as proteomics, protein design and host-virus interactions.

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