Title

Designing Epitope Ensemble Vaccines against TB by Selection: Prioritizing Antigens using Predicted
Immunogenicity

Authors

Jaymisha Mistry and Darren R Flower*

Affiliation

¹School of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, United Kingdom, B4 7ET.

E-mail

d.r.flower@aston.ac.uk

Article Type

Hypothesis

Date

Received July 3, 2017; Revised July 4, 2017; Accepted July 4, 2017; Published July 31, 2017

Tuberculosis (TB) is a global health burden, and a major cause of mortality and morbidity in West Africa. Here, we select key conserved pathogen epitopes of proven immunogenicity to form a potential TB epitope ensemble vaccine. We compared two vaccine formulations: one comprising class I epitopes from the 13 most prevalent class I epitope-bearing antigens and class II epitopes deriving from the 20 most prevalent class II epitope-bearing antigens and another consisting of epitopes derived solely from 5 antigens identified as the most immunogenic by VaxiJen. In the prevalence analysis, 279 class I and 561 class II epitopes were collected and a subset selected using our published methodology, yielding 32 conserved epitopes. Combining 9 conserved epitopes gave a putative vaccine with predicted population coverage (PPC) over 95%. This consists of ISSGVFLLK, AVAGAAILV, WYYQSGLSI, YQSGLSIVM, MPVGGQSSF, QSSFYSDWY, WDINTPAFEWYYQSGLSIVM, DAPLITNPGGLLEQAVAVEE and NQAVTPAARALPLTSLTSAA. 5 immunogenic antigens VaxiJen-identified yielded 187 epitopes, which we again analyzed using published protocol. This identified 11 conserved epitopes. From this set the highest PPC value (<85%) was obtained by combining: GQQYQAMSAQAAAFH, DDIKATYDKGILTVSVAVSE and AVAGAAILV. We conclude that prioritizing epitope selection using predicted immunogenicity alone is likely to be unduly restrictive and is currently not an optimal or advisable strategy in the design of epitope ensemble vaccines.

Keywords:

Designing epitope, Ensemble vaccines, TB, antigens, immunogenicity

Citation

Jaymisha Mistry & Darren R Flower, Bioinformation 13(7): 220-223 (2017)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.