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      Prioritization of potential vaccine targets using comparative proteomics and designing of the chimeric multi-epitope vaccine against Pseudomonas aeruginosa

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      Scientific Reports
      Nature Publishing Group UK

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          Abstract

          Multidrug-resistant Pseudomonas aeruginosa is one of the worldwide health problems involved in elevated mortality and morbidity. Therefore, it is important to find a therapeutic for this pathogen. In the present study, we have designed a chimeric vaccine against P. aeruginosa with the help of comparative proteomics and reverse vaccinology approaches. Using comparative subtractive proteomic analysis of 1,191 proteomes of P. aeruginosa, a total of twenty unique non-redundant proteomes were selected. In these proteomes, fifteen outer membrane proteins (OMPs) of P. aeruginosa were selected based on the basis of hydrophilicity, non-secretory nature, low transmembrane helix (<1), essentiality, virulence, pathway association, antigenic, and protein-protein network analysis. Reverse vaccinology approach was used to identify antigenic and immunogenic MHC class I, MHC class II and B cell epitopes present in the selected OMPs that can enhance T cell and B cell mediated immunogenicity. The selected epitopes were shortlisted based on their allergenicity, toxicity potentials, solubility, and hydrophilicity analysis. Immunogenic peptides were used to design a multi-epitope vaccine construct. Immune-modulating adjuvants and PADRE (Pan HLA-DR epitopes) sequence were added with epitopes sequence to enhance the immunogenicity. All the epitopes, adjuvants and PADRE sequence were joined by linkers. The designed vaccine constructs (VT1, VT2, VT3, and VT4) were analyzed by their physiochemical properties using different tools. Selected chimeric vaccine constructs (VT1, VT3, and VT4) were further shortlisted by their docking score with different HLA alleles. The final selected VT4 construct was docked with TLR4/MD2 complex and confirmed by molecular dynamics simulation studies. The final vaccine VT-4 construct was in-silico cloned in pET28a. Therefore, the designed construct VT4 may be studied to control the interaction of P. aeruginosa with host and infection caused by P. aeruginosa.

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          Most cited references45

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          Prediction of protein subcellular localization.

          Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
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            JCat: a novel tool to adapt codon usage of a target gene to its potential expression host

            A novel method for the adaptation of target gene codon usage to most sequenced prokaryotes and selected eukaryotic gene expression hosts was developed to improve heterologous protein production. In contrast to existing tools, JCat (Java Codon Adaptation Tool) does not require the manual definition of highly expressed genes and is, therefore, a very rapid and easy method. Further options of JCat for codon adaptation include the avoidance of unwanted cleavage sites for restriction enzymes and Rho-independent transcription terminators. The output of JCat is both graphically and as Codon Adaptation Index (CAI) values given for the pasted sequence and the newly adapted sequence. Additionally, a list of genes in FASTA-format can be uploaded to calculate CAI values. In one example, all genes of the genome of Caenorhabditis elegans were adapted to Escherichia coli codon usage and further optimized to avoid commonly used restriction sites. In a second example, the Pseudomonas aeruginosa exbD gene codon usage was adapted to E.coli codon usage with parallel avoidance of the same restriction sites. For both, the degree of introduced changes was documented and evaluated. JCat is integrated into the PRODORIC database that hosts all required information on the various organisms to fulfill the requested calculations. JCat is freely accessible at .
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              Scalable web services for the PSIPRED Protein Analysis Workbench

              Here, we present the new UCL Bioinformatics Group’s PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. All software and services are available via the UCL Bioinformatics Group website at http://bioinf.cs.ucl.ac.uk/.
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                Author and article information

                Contributors
                vishvanath7@yahoo.co.in
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 March 2019
                27 March 2019
                2019
                : 9
                : 5240
                Affiliations
                ISNI 0000 0004 1764 745X, GRID grid.462331.1, Department of Biochemistry, , Central University of Rajasthan, ; Ajmer, 305817 India
                Author information
                http://orcid.org/0000-0003-1664-3871
                Article
                41496
                10.1038/s41598-019-41496-4
                6437148
                30918289
                5864fcc2-3872-4125-8df2-e4611b23e7a4
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 November 2018
                : 11 March 2019
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