Zika virus (ZIKV) is an aedes mosquito borne pathogen belonging to the member of flaviviridae subgroup is the causative agent of an emerging disease called Zika fever, known as a benign infection usually presenting as influenza like illness with cutaneous rash. Due to recent epidemic outbreaks it is realized as a major health risk which need enhanced surveillance, but no attempt has been made to design an epitope based peptide vaccine against Zika virus. Viral envelope proteins are derived from host cell membrane proteins with some viral glycoproteins and are used to cover their protective protein capsid, help the viruses to enter host cells and help them to avoid the host immune response. In this study, amino acid sequence of ZIKV envelope glycoprotein was obtained from a protein database and examined with in silico approaches to determine the most immunogenic epitopes for B cell and T cell which could induce humoral as well as cell mediated immune response. Both the linear and conformational epitopes for B cell were predicted by immunoinformatics tools housed in IEDB resources. The peptide sequence DAHAKRQTVVVLGSQEGAV from position 121 and peptide sequence from 117-137 amino acids were predicted as most potential B cell linear and conformational epitopes respectively. Epitopes for CD4+ and CD8+ T cell were also predicted by using tools within IEDB resource and peptide sequence MMLELDPPF from position 250-258 amino acids was predicted as most immunogenic CD8+ T cell epitope with immune response evoking ability prediction score (I pMHC) of 0.09139 and conservancy of 52.17%. The innate immune response for ZIKV envelope glycoprotein was determined by interferon (IFN)-gamma effectuation and mimicking capacity by immunoinformatics and molecular docking study respectively. However, this is an introductory approach to design an epitope based peptide vaccine against Zika virus; we hope this model will be very much helpful in designing and predicting novel vaccine candidate.

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Staphylococcus aureus infection is a leading cause of mortality and morbidity in community, hospital and live-stock sectors, especially with the widespread emergence of methicillin-resistant S. aureus (MRSA) strains. To identify new drug molecules to treat MRSA patients, we have undertaken to search essential proteins that are indispensable for their survival but nonhomologous to human host proteins. The current study utilizes a subtractive genome and proteome approach to screen the possible therapeutic targets against S. aureus USA300. Bacterial essential genes are obtained from the DEG database and are compared to avoid cross-reactivity with human host genes. In silico analysis shows 198 proteins that may be considered as therapeutic candidates. Depending on their sub-cellular localization, proteins are grouped as either vaccine or drug targets or both. Extracellular proteins such as cell division proteins (Q2FZ91, Q2FZ95), penicillin-binding proteins (Q2FZ94, Q2FYI0) of the bacterial cell wall, phosphoglucomutase (Q2FE11) and lipoteichoic acid synthase (Q2FIS2) are considered as vaccine targets, and their epitopes have been mapped. Altogether, 53 drug targets are identified, which have shown similarity with the drug targets available in the DrugBank database. Predicted drug targets belong to the common metabolic pathways of MRSA, such as fatty acid biosynthesis, folate biosynthesis, peptidoglycan biosynthesis, ribosome, etc. Protein-protein interaction analysis emphasizing peptidoglycan biosynthesis reveals the connection between penicillin-binding proteins, mur-family proteins and FemXAB proteins. In this study, staphylococcal FemA protein (P0A0A5) is subjected to structure-based virtual screening for the drug repurposing approach. There are 20 residues missing in the crystal structure of FemA, and 12 of these residues are located at the catalytic site. The missing residues are modelled, and stereochemistry is checked. FDA approved drugs available in the DrugBank database have been used in virtual screening with FemA in search of potential repurposed molecules. This approach provides us with 10 drugs that may be used in the treatment of methicillin-resistant staphylococcal mediated diseases. AutoDock 4.2 is used for in silico screening and shows a comparable inhibition constant (Ki) for all 10 FDA-approved drugs towards FemA. Most of these drugs are used in the treatment of various cancers, migraines and leukaemia. Protein-drug interaction analysis shows that the drugs mostly interact with hydrophobic residues of FemA. Moreover, Tyr328 and Lys383 contribute largely to hydrogen bondings during interactions. All interacting amino acids that bind to the drugs are part of the active site cavity of FemA.

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