Anticancer screening of medicinal plant phytochemicals against Cyclin-Dependent Kinase-2 (CDK2): An in-silico approach

Background: Cyclin-Dependent Kinase-2 (CDK2) is a member of serine/threonine protein kinases family and plays an important role in regulation of various eukaryotic cell division events. Over-expression of CDK2 during cell cycle may lead to several cellular functional aberrations including diverse types of cancers (lung cancer, primary colorectal carcinoma, ovarian cancer, melanoma and pancreatic carcinoma) in humans. Medicinal plants phytochemicals which have anticancer potential can be used as an alternative drug resource.Methods: This study was designed to find out anticancer phytochemicals from medicinal plants which could inhibit CDK2 with the help of molecular docking technique. Molecular Operating Environment (MOE v2009) software was used to dock 2300 phytochemicals in this study.Results: The outcome of this study shows that four phytochemicals Kushenol T, Remangiflavanone B, Neocalyxins A and Elenoside showed the lowest S-score (-17.83, -17.57, -17.26, -17.17 respectively) and binds strongly with all eight active residues Tyr15, Lys33, Ileu52, Lys56, Leu78, phe80, Asp145 and Phe146 of CDK2 binding site. These phytochemicals could successfully inhibit the CDK2.Conclusion: These phytochemicals can be considered as potential anticancer agents and used in drug development against CDK2. We anticipate that this study would pave way for phytochemical based novel small molecules as more efficacious and selective anti-cancer therapeutic compounds

PCR Primer Design for In-Silico Rapid Detection of Ocular Infection Caused by Candida Species in Humans

Background: Computational analyses have shown great potentials for providing tools for the rapid detection and identification of fungi for medical, scientific and commercial purposes. Various bioinformatics tools have been developed for finding the specific regions within the ribosomal RNA (rRNA) gene complex. Candida is a genus of yeast that includes about 150 different species and is the most common cause of human ocular infections. In the present study, rapid detection method of Candida, based on specific regions (18S, 5.8S and 28S) of ribosomal RNA (rRNA) genes of eight (8) species e.g. C. albicans, C. krusei, C. parapsilosis, C. glabrata, C. guilliermondii, C. kefyr, C. lusitaniae and C. tropicalis has been developed. Rapid diagnosis and early identification of causative agent through computational based methods with high accuracy will result in effective treatment.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Objective: Development of rapid detection method and assay for Candida species based on bioinformatics tools.Methodology: Ribosomal RNA (18S, 5.8S and 28S) sequences of eight Candida species were retrieved from GenBank/EMBL databases. A set of unique primers were designed based on the conserved region in the given yeast species. To verify the in-silico specificity of the designed primers, the NCBI-BLAST program was employed to search the primers in short, near exact sequences. The primers were further analyzed by the AmplifX tool to determine their specificity and sensitivity against Candida species.Conclusions: The study resulted in the development of rapid and reproducible detection strategy of Candida species on the basis of computational PCR that will be very helpful for the doctors/practitioners to prescribe targeted medicine against Candida and related causative agents.</p

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches.

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated