Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design

Introduction: Tuberculosis (TB) is a communicable disease caused by Mycobacterium tuberculosis. Bacillus Calmette-Guérin is the only vaccine available for TB. However, although the vaccine effectively protects children from TB, its efficacy in adults is still debatable. No effective vaccine is prese...

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Main Authors: Esakkimuthu THANGAMARIAPPAN, Manikandan MOHAN, Krishnan SUNDAR
Format: Article
Language:Turkish
Published: Galenos Yayinevi 2021-12-01
Series:Mediterranean Journal of Infection, Microbes and Antimicrobials
Subjects:
Online Access:http://mjima.org/abstract.php?id=240
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author Esakkimuthu THANGAMARIAPPAN
Manikandan MOHAN
Krishnan SUNDAR
author_facet Esakkimuthu THANGAMARIAPPAN
Manikandan MOHAN
Krishnan SUNDAR
author_sort Esakkimuthu THANGAMARIAPPAN
collection DOAJ
description Introduction: Tuberculosis (TB) is a communicable disease caused by Mycobacterium tuberculosis. Bacillus Calmette-Guérin is the only vaccine available for TB. However, although the vaccine effectively protects children from TB, its efficacy in adults is still debatable. No effective vaccine is presently available to prevent TB. An effective vaccine should provoke humoral immunity to prevent the adhesion of M. tuberculosis to macrophages. In this context, B-cell epitopes may play an important role in vaccine development. Hence, this study aimed to identify B-cell epitopes using in silico tools. Materials and Methods: In this study, B-cell epitopes were predicted using two tools (ABCPred and BCPREDS), which consists of three methods (artificial neural networks, BCPred, and AAP). Further, the epitopes predicted by the three prediction methods were analyzed for overlapping, and the ToxinPred, VaxiJen and AllerTop servers were used for analysis. Results: A total of 2003 epitopes were predicted using all the prediction methods. Among these, 80 epitopes were predicted as overlapping epitopes, and 80, 57, and 29 epitopes were screened using the ToxinPred, VaxiJen, and AllerTop tools, respectively. Conclusion: The epitopes predicted in the current study needs to be further validated using in vitro and in vivo analyses for B-cell response toward infection by M. tuberculosis.
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spelling doaj.art-05f5c726051449f1bc3c15969a3d09282023-02-15T16:07:22ZturGalenos YayineviMediterranean Journal of Infection, Microbes and Antimicrobials2147-673X2021-12-0110110.4274/mjima.galenos.2021.2021.23Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine DesignEsakkimuthu THANGAMARIAPPAN0https://orcid.org/0000-0003-1546-6279Manikandan MOHAN1https://orcid.org/0000-0002-0132-7510Krishnan SUNDAR2https://orcid.org/0000-0001-7156-1057Kalasalingam Academy of Research and Education, School of Bio and Chemical Enginnering, Department of Biotechnology, Krishnankoil, IndiaKalasalingam Academy of Research and Education, School of Bio and Chemical Enginnering, Department of Biotechnology, Krishnankoil, IndiaKalasalingam Academy of Research and Education, School of Bio and Chemical Enginnering, Department of Biotechnology, Krishnankoil, IndiaIntroduction: Tuberculosis (TB) is a communicable disease caused by Mycobacterium tuberculosis. Bacillus Calmette-Guérin is the only vaccine available for TB. However, although the vaccine effectively protects children from TB, its efficacy in adults is still debatable. No effective vaccine is presently available to prevent TB. An effective vaccine should provoke humoral immunity to prevent the adhesion of M. tuberculosis to macrophages. In this context, B-cell epitopes may play an important role in vaccine development. Hence, this study aimed to identify B-cell epitopes using in silico tools. Materials and Methods: In this study, B-cell epitopes were predicted using two tools (ABCPred and BCPREDS), which consists of three methods (artificial neural networks, BCPred, and AAP). Further, the epitopes predicted by the three prediction methods were analyzed for overlapping, and the ToxinPred, VaxiJen and AllerTop servers were used for analysis. Results: A total of 2003 epitopes were predicted using all the prediction methods. Among these, 80 epitopes were predicted as overlapping epitopes, and 80, 57, and 29 epitopes were screened using the ToxinPred, VaxiJen, and AllerTop tools, respectively. Conclusion: The epitopes predicted in the current study needs to be further validated using in vitro and in vivo analyses for B-cell response toward infection by M. tuberculosis.http://mjima.org/abstract.php?id=240epitopesb-cellmycobacterium tuberculosisvaccine
spellingShingle Esakkimuthu THANGAMARIAPPAN
Manikandan MOHAN
Krishnan SUNDAR
Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design
Mediterranean Journal of Infection, Microbes and Antimicrobials
epitopes
b-cell
mycobacterium tuberculosis
vaccine
title Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design
title_full Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design
title_fullStr Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design
title_full_unstemmed Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design
title_short Computational Prediction of B-cell Epitopes of Mycobacterium tuberculosis-Implications in Vaccine Design
title_sort computational prediction of b cell epitopes of mycobacterium tuberculosis implications in vaccine design
topic epitopes
b-cell
mycobacterium tuberculosis
vaccine
url http://mjima.org/abstract.php?id=240
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AT krishnansundar computationalpredictionofbcellepitopesofmycobacteriumtuberculosisimplicationsinvaccinedesign