Identification of Potential MHC Class-II-Restricted Epitopes Derived from  Leishmania donovani  Antigens by Reverse Vaccinology and Evaluation of Their CD4+ T-Cell Responsiveness against Visceral Leishmaniasis

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Abstract

Visceral leishmaniasis (VL) is one of the most neglected tropical diseases for which no vaccine exists. In spite of extensive efforts, no successful vaccine is available against this dreadful infectious disease. To support vaccine development, an immunoinformatics approach was applied to screen potential MHC class-II-restricted epitopes that can activate the immune cells. Initially, 37 epitopes derived from six stage-dependent, overexpressed antigens were predicted, which were presented by at least 26 diverse MHC class-II allele. Based on a population coverage analysis and human leukocyte antigen cross-presentation ability, six of the 37 epitopes were selected for further analysis. Stimulation with synthetic peptide alone or as a cocktail triggered intracellular IFN-γ production. Moreover, specific IgG antibodies were detected in the serum of active VL cases against P1, P4, P5, and P6 in order to evaluate the peptide effect on the humoral immune response. Additionally, most of the peptides, except P2, were found to be non-inducers of CD4+ IL-10 against both active VL as well as treated VL subjects. This finding suggests there is no role of these peptides in the pathogenesis of Leishmania. Peptide immunogenicity was validated in BALB/c mice immunized with a cocktail of synthetic peptide emulsified in complete Freund’s adjuvant/incomplete Freund’s adjuvant. The immunized splenocytes induced strong spleen cell proliferation upon parasite re-stimulation. Furthermore, increased IFN-γ, interleukin-12, IL-17, and IL-22 production augmented with elevated nitric oxide (NO) synthesis is thought to play a crucial role in macrophage activation. In this investigation, we identified six MHC class-II-restricted epitope hotspots of Leishmania antigens that induce CD4+ Th1 and Th17 responses, which could be used to potentiate a human universal T-epitope vaccine against VL.

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Designing of interferon-gamma inducing MHC class-II binders

Sandeep Kumar Dhanda, Pooja Vir, Gajendra Raghava (2013)

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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NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

Morten Bundgaard-Nielsen, Massimo Andreatta (2016)

Background Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Results Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. Conclusions We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0288-x) contains supplementary material, which is available to authorized users.

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NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

Morten A. Nielsen, Ole Lund (2009)

Background The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

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Author and article information

Contributors

Manas Ranjan Dikhit: URI : http://frontiersin.org/people/u/360304

Sushmita Das: URI : http://frontiersin.org/people/u/287988

Budheswar Dehury: URI : http://frontiersin.org/people/u/129670

Ajaya Kumar Rout: URI : http://frontiersin.org/people/u/505185

Fauzia Jamal: URI : http://frontiersin.org/people/u/357905

Ganesh Chandra Sahoo: URI : http://frontiersin.org/people/u/100444

Krishna Pandey: URI : http://frontiersin.org/people/u/504676

V. N. R. Das: URI : http://frontiersin.org/people/u/504443

Sanjiva Bimal: URI : http://frontiersin.org/people/u/504675

Pradeep Das: URI : http://frontiersin.org/people/u/408900

Journal

Journal ID (nlm-ta): Front Immunol

Journal ID (iso-abbrev): Front Immunol

Journal ID (publisher-id): Front. Immunol.

Title: Frontiers in Immunology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-3224

Publication date (Electronic): 14 December 2017

Publication date Collection: 2017

Volume: 8

Electronic Location Identifier: 1763

Affiliations

[1] ¹BioMedical Informatics Division, Rajendra Memorial Research Institute of Medical Sciences , Patna, India

[2] ²Department of Immunology, Rajendra Memorial Research Institute of Medical Sciences , Patna, India

[3] ³Department of Microbiology, All India Institute of Medical Sciences , Patna, India

[4] ⁴Biomedical Informatics Centre, ICMR-Regional Medical Research Centre , Odisha, India

[5] ⁵Biotechnology Laboratory, ICAR-Central Inland Fisheries Research Institute , Kolkata, India

[6] ⁶Department of Microbiology, Rajendra Memorial Research Institute of Medical Sciences , Patna, India

[7] ⁷Department of Clinical Medicine, Rajendra Memorial Research Institute of Medical Sciences , Patna, India

[8] ⁸Department of Molecular Parasitology, Rajendra Memorial Research Institute of Medical Sciences , Patna, India

Author notes

Edited by: Abdul Qader Abbady, Atomic Energy Commission of Syria, Syria

Reviewed by: Henry Muriuki Kariithi, International Atomic Energy Agency, Austria; Benoit Stijlemans, Vrije Universiteit Brussel, Belgium

*Correspondence: Sanjiva Bimal, drsbimal24@ 123456yahoo.com ; Pradeep Das, drpradeep.das@ 123456gmail.com

Specialty section: This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology

Article

DOI: 10.3389/fimmu.2017.01763

PMC ID: 5735068

PubMed ID: 29312304

SO-VID: dbebe4d2-0425-4412-94f8-e3cf27f18a2a

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 25 August 2017

Date accepted : 27 November 2017

Page count

Figures: 7, Tables: 4, Equations: 0, References: 74, Pages: 17, Words: 11652

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Identification of Potential MHC Class-II-Restricted Epitopes Derived from Leishmania donovani Antigens by Reverse Vaccinology and Evaluation of Their CD4+ T-Cell Responsiveness against Visceral Leishmaniasis

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Malaria vaccine development

Most cited references 64

Designing of interferon-gamma inducing MHC class-II binders

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

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Cited by 21

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