Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host–virus protein–protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection.Objective: This study aims to identify therapeutic target proteins in humans that could act as virus–host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors.Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 (“hCoV-2”), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient’s mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein.Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein.Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention

An Update on the Therapeutic Anticancer Potential of <i>Ocimum sanctum</i> L.: “Elixir of Life”

In most cases, cancer develops due to abnormal cell growth and subsequent tumour formation. Due to significant constraints with current treatments, natural compounds are being explored as potential alternatives. There are now around 30 natural compounds under clinical trials for the treatment of cancer. Tulsi, or Holy Basil, of the genus Ocimum, is one of the most widely available and cost-effective medicinal plants. In India, the tulsi plant has deep religious and medicinal significance. Tulsi essential oil contains a valuable source of bioactive compounds, such as camphor, eucalyptol, eugenol, alpha-bisabolene, beta-bisabolene, and beta-caryophyllene. These compounds are proposed to be responsible for the antimicrobial properties of the leaf extracts. The anticancer effects of tulsi (Ocimum sanctum L.) have earned it the title of “queen of herbs” and “Elixir of Life” in Ayurvedic treatment. Tulsi leaves, which have high concentrations of eugenol, have been shown to have anticancer properties. In a various cancers, eugenol exerts its antitumour effects through a number of different mechanisms. In light of this, the current review focuses on the anticancer benefits of tulsi and its primary phytoconstituent, eugenol, as apotential therapeutic agent against a wide range of cancer types. In recent years, tulsi has gained popularity due to its anticancer properties. In ongoing clinical trials, a number of tulsi plant compounds are being evaluated for their potential anticancer effects. This article discusses anticancer, chemopreventive, and antioxidant effects of tulsi

Stability indicating HPLC method for the simultaneous analysis of Gatifloxacin and Loteprednol in eyedrop formulation using design of experiment approach

187-197Design of experiment (DOE) assisted simple, rapid, precise and accurate stability indicating HPLC method has been developed for simultaneous estimation of Gatifloxacin (GTF) and Loteprednol (LOT) along with their forced degradation products. The developed method has been optimized and developed by using central composite design (CCD) in response surface methodology (RSM). Trails have been undertaken and ratio of phosphate buffer in mobile phase, pH of buffer and flow rate are selected as factors. Resolution, tailing factor (GTF) and tailing factor (LOTE) are selected for determining the system response in the process of method optimization. The responses have been optimized using the Derringer’s desirability function. The effective separation is achieved on Phenomenex EVO-C18 column (250 mm x 4.6 mm i.d, 5 μm particle size) with mobile composed of 10 mM phosphate buffer, pH 3.5 and organic phase composed of mixture of acetonitrile and methanol 60:40 % v/v, the flow rate was 1.0 mL/min, the signals were detected at 267 nm. The developed method was validated for linearity, accuracy, precision, and robustness. The method was applied successfully for stability samples

Table3_Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins.xlsx

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host–virus protein–protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection.Objective: This study aims to identify therapeutic target proteins in humans that could act as virus–host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors.Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 (“hCoV-2”), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient’s mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein.Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein.Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.</p

Table1_Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins.xlsx

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host–virus protein–protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection.Objective: This study aims to identify therapeutic target proteins in humans that could act as virus–host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors.Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 (“hCoV-2”), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient’s mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein.Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein.Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.</p

Table4_Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins.xlsx

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host–virus protein–protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection.Objective: This study aims to identify therapeutic target proteins in humans that could act as virus–host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors.Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 (“hCoV-2”), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient’s mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein.Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein.Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.</p

Table2_Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins.xlsx

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host–virus protein–protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection.Objective: This study aims to identify therapeutic target proteins in humans that could act as virus–host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors.Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 (“hCoV-2”), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient’s mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein.Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein.Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.</p