Shalini Maurya, Salman Akhtar, Mohammad Kalim Ahmad Khan
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引用次数: 0
Abstract
Purpose: Multidrug-resistant Burkholderia pseudomallei is associated with significant morbidity and mortality. Hence, there is a requirement for a vaccine for this pathogen. Using subtractive proteomics and reverse vaccinology approaches, we have designed a chimeric multiepitope vaccine against the pathogen in the present study. Methods: Twenty-one non-redundant pathogen proteomes were mined using a subtractive proteomics strategy. Out of these, by various analyses, we found proteins that were non-homologous to humans, essential, and virulent. BLASTp against the PDB database and Pocket druggability analysis yielded nine proteins whose 3D structure is available and are druggable. Four proteins that could be candidates for vaccines were identified by subcellular localization and antigenicity prediction, and they could be used in reverse vaccinology methods to create a chimeric multiepitope vaccine. Results: Using online resources and servers, MHC class I, II, and B cell epitopes were identified. The predicted epitopes were selected based on analysis of toxicity, solubility, allergenicity, and hydrophilicity. These predicted epitopes, which were immunogenic, were used for the construction of a multivalent chimeric vaccine. The epitopes, adjuvants, linkers, and PADRE amino acid sequences were employed to create the vaccine. Shortlisted vaccine constructs also interact with the HLA allele and TLR4, as evident from docking and molecular dynamics simulation. Thus, vaccine construct V1 can elicit an immune response against Burkholderia pseudomallei . Conclusion: The availability of the proteome of B. pseudomallei has made this study possible through the usage of various in silico approaches. We could shortlist vaccine targets using subtractive proteomics and then construct chimeric vaccines using reverse vaccinology and immunoinformatics approaches.
期刊介绍:
The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial studies and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases. The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.