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      Immunoinformatics-Aided Design and Evaluation of a Potential Multi-Epitope Vaccine against Klebsiella Pneumoniae


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          Klebsiella pneumoniae is an opportunistic gram-negative bacterium that causes nosocomial infection in healthcare settings. Despite the high morbidity and mortality rate associated with these bacterial infections, no effective vaccine is available to counter the pathogen. In this study, the pangenome of a total of 222 available complete genomes of K. pneumoniae was explored to obtain the core proteome. A reverse vaccinology strategy was applied to the core proteins to identify four antigenic proteins. These proteins were then subjected to epitope mapping and prioritization steps to shortlist nine B-cell derived T-cell epitopes which were linked together using GPGPG linkers. An adjuvant (Cholera Toxin B) was also added at the N-terminal of the vaccine construct to improve its immunogenicity and a stabilized multi-epitope protein structure was obtained using molecular dynamics simulation. The designed vaccine exhibited sustainable and strong bonding interactions with Toll-like receptor 2 and Toll-like receptor 4. In silico reverse translation and codon optimization also confirmed its high expression in E. coli K12 strain. The computer-aided analyses performed in this study imply that the designed multi-epitope vaccine can elicit specific immune responses against K. pneumoniae. However, wet lab validation is necessary to further verify the effectiveness of this proposed vaccine candidate.

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          Most cited references55

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          Structure validation by Calpha geometry: phi,psi and Cbeta deviation.

          Geometrical validation around the Calpha is described, with a new Cbeta measure and updated Ramachandran plot. Deviation of the observed Cbeta atom from ideal position provides a single measure encapsulating the major structure-validation information contained in bond angle distortions. Cbeta deviation is sensitive to incompatibilities between sidechain and backbone caused by misfit conformations or inappropriate refinement restraints. A new phi,psi plot using density-dependent smoothing for 81,234 non-Gly, non-Pro, and non-prePro residues with B < 30 from 500 high-resolution proteins shows sharp boundaries at critical edges and clear delineation between large empty areas and regions that are allowed but disfavored. One such region is the gamma-turn conformation near +75 degrees,-60 degrees, counted as forbidden by common structure-validation programs; however, it occurs in well-ordered parts of good structures, it is overrepresented near functional sites, and strain is partly compensated by the gamma-turn H-bond. Favored and allowed phi,psi regions are also defined for Pro, pre-Pro, and Gly (important because Gly phi,psi angles are more permissive but less accurately determined). Details of these accurate empirical distributions are poorly predicted by previous theoretical calculations, including a region left of alpha-helix, which rates as favorable in energy yet rarely occurs. A proposed factor explaining this discrepancy is that crowding of the two-peptide NHs permits donating only a single H-bond. New calculations by Hu et al. [Proteins 2002 (this issue)] for Ala and Gly dipeptides, using mixed quantum mechanics and molecular mechanics, fit our nonrepetitive data in excellent detail. To run our geometrical evaluations on a user-uploaded file, see MOLPROBITY (http://kinemage.biochem.duke.edu) or RAMPAGE (http://www-cryst.bioc.cam.ac.uk/rampage). Copyright 2003 Wiley-Liss, Inc.
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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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              Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

              B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/. Proteins 2006. (c) 2006 Wiley-Liss, Inc.

                Author and article information

                Vaccines (Basel)
                Vaccines (Basel)
                12 August 2019
                September 2019
                : 7
                : 3
                : 88
                [1 ]Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
                [2 ]Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan 60800, Pakistan
                [3 ]State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health (GIBH), Chinese Academy of Sciences (CAS), Guangzhou 510530, China
                Author notes
                [* ]Correspondence: amjad.ali@ 123456asab.nust.edu.pk ; Tel.: +92-3339191903
                Author information
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                : 15 July 2019
                : 06 August 2019

                klebsiella pneumoniae,pangenome,reverse vaccinology,potential vaccine candidate,immunoinformatics,multi-epitope vaccine


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