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      Novel Immunoinformatics Approaches to Design Multi-epitope Subunit Vaccine for Malaria by Investigating Anopheles Salivary Protein

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          Abstract

          Malaria fever has been pervasive for quite a while in tropical developing regions causing high morbidity and mortality. The causal organism is a protozoan parasite of genus Plasmodium which spreads to the human host by the bite of hitherto infected female Anopheles mosquito. In the course of biting, a salivary protein of Anopheles helps in blood feeding behavior and having the ability to elicit the host immune response. This study represents a series of immunoinformatics approaches to design multi-epitope subunit vaccine using Anopheles mosquito salivary proteins. Designed subunit vaccine was evaluated for its immunogenicity, allergenicity and physiochemical parameters. To enhance the stability of vaccine protein, disulfide engineering was performed in a region of high mobility. Codon adaptation and in silico cloning was also performed to ensure the higher expression of designed subunit vaccine in E. coli K12 expression system. Finally, molecular docking and simulation study was performed for the vaccine protein and TLR-4 receptor, to determine the binding free energy and complex stability. Moreover, the designed subunit vaccine was found to induce anti-salivary immunity which may have the ability to prevent the entry of Plasmodium sporozoites into the human host.

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          Designing of interferon-gamma inducing MHC class-II binders

          Background The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4+ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides. Results It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC. Conclusion Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/). Reviewers This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai.
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            Cerebral malaria: mechanisms of brain injury and strategies for improved neurocognitive outcome.

            Cerebral malaria is the most severe neurological complication of infection with Plasmodium falciparum. With >575,000 cases annually, children in sub-Saharan Africa are the most affected. Surviving patients have an increased risk of neurological and cognitive deficits, behavioral difficulties, and epilepsy making cerebral malaria a leading cause of childhood neurodisability in the region. The pathogenesis of neurocognitive sequelae is poorly understood: coma develops through multiple mechanisms and there may be several mechanisms of brain injury. It is unclear how an intravascular parasite causes such brain injury. Understanding these mechanisms is important to develop appropriate neuroprotective interventions. This article examines possible mechanisms of brain injury in cerebral malaria, relating this to the pathogenesis of the disease, and explores prospects for improved neurocognitive outcome.
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              AllerTOP - a server for in silico prediction of allergens

              Background Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences. Results A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1 , z2 and z3 ) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity. Conclusions AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin.
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                Author and article information

                Contributors
                vkprajapati@curaj.ac.in
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 January 2018
                18 January 2018
                2018
                : 8
                : 1125
                Affiliations
                [1 ]ISNI 0000 0004 1764 745X, GRID grid.462331.1, Department of Biochemistry, , School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, ; Ajmer, 305817 Rajasthan, India
                [2 ]ISNI 0000 0004 1764 745X, GRID grid.462331.1, Department of Biotechnology, , School of Life Sciences, Central University of Rajasthan, Bandarsindri, Kishangarh, ; Ajmer, 305817 Rajasthan, India
                Article
                19456
                10.1038/s41598-018-19456-1
                5773588
                29348555
                566f60c1-3c02-4599-b2d2-f06e74ed784d
                © The Author(s) 2018

                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
                : 22 September 2017
                : 2 January 2018
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