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    Rights statement: This is the author’s version of a work that was accepted for publication in Molecular Immunology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Molecular Immunology, 97, 2018 DOI: 10.1016/j.molimm.2018.03.007

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In silico design of Mycobacterium tuberculosis epitope ensemble vaccines

Research output: Contribution to journalJournal articlepeer-review

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  • Preksha Shah
  • Jaymisha Mistry
  • Pedro A. Reche
  • Derek Gatherer
  • Darren R. Flower
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<mark>Journal publication date</mark>05/2018
<mark>Journal</mark>Molecular Immunology
Volume97
Number of pages7
Pages (from-to)56-62
Publication StatusPublished
Early online date19/03/18
<mark>Original language</mark>English

Abstract

Abstract Effective control of Mycobacterium tuberculosis is a global necessity. In 2015, tuberculosis (TB) caused more deaths than HIV. Considering the increasing prevalence of multi-drug resistant forms of M. tuberculosis, the need for effective TB vaccines becomes imperative. Currently, the only licensed TB vaccine is Bacillus Calmette-Guérin (BCG). Yet, BCG has many drawbacks limiting its efficacy and applicability. We applied advanced computational procedures to derive a universal TB vaccine and one targeting East Africa. Our approach selects an optimal set of highly conserved, experimentally validated epitopes, with high projected population coverage (PPC). Through rigorous data analysis, five different potential vaccine combinations were selected each with PPC above 80% for East Africa and above 90% for the World. Two potential vaccines only contained CD8+ epitopes, while the others included both CD4+ and CD8+ epitopes. Our prime vaccine candidate was a putative seven-epitope ensemble comprising: SRGWSLIKSVRLGNA, KPRIITLTMNPALDI, AAHKGLMNIALAISA, FPAGGSTGSL, MLLAVTVSL, QSSFYSDW and KMRCGAPRY, with a 97.4% global PPC and a 92.7% East African PPC.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Molecular Immunology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Molecular Immunology, 97, 2018 DOI: 10.1016/j.molimm.2018.03.007