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Joint epitope selection and spacer design for string-of-beads vaccines.

Bioinformatics 36, 2, i643-i650 (2020)
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MOTIVATION: Conceptually, epitope-based vaccine design poses two distinct problems: (i) selecting the best epitopes to elicit the strongest possible immune response and (ii) arranging and linking them through short spacer sequences to string-of-beads vaccines, so that their recovery likelihood during antigen processing is maximized. Current state-of-the-art approaches solve this design problem sequentially. Consequently, such approaches are unable to capture the inter-dependencies between the two design steps, usually emphasizing theoretical immunogenicity over correct vaccine processing, thus resulting in vaccines with less effective immunogenicity in vivo. RESULTS: In this work, we present a computational approach based on linear programming, called JessEV, that solves both design steps simultaneously, allowing to weigh the selection of a set of epitopes that have great immunogenic potential against their assembly into a string-of-beads construct that provides a high chance of recovery. We conducted Monte Carlo cleavage simulations to show that a fixed set of epitopes often cannot be assembled adequately, whereas selecting epitopes to accommodate proper cleavage requirements substantially improves their recovery probability and thus the effective immunogenicity, pathogen and population coverage of the resulting vaccines by at least 2-fold. AVAILABILITY AND IMPLEMENTATION: The software and the data analyzed are available at https://github.com/SchubertLab/JessEV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Proteasome; Predictions; Framework; Cleavage; Affinity; Antigen; Trial
ISSN (print) / ISBN 1367-4803
Journal Bioinformatics
Quellenangaben Volume: 36, Issue: , Pages: i643-i650, Article Number: , Supplement: 2
Publisher Oxford University Press
Publishing Place Oxford
Reviewing status Peer reviewed
Grants Postdoctoral Fellowship Program of the Helmholtz Zentrum Munchen
Helmholtz Association under the joint research school 'Munich School for Data Science' (MuDS, Helmholtz Association)