Faculty of Mathematics Informatics and Statistics, Ludwig Maximilian Universität, München 80333, Germany.
Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg 85764, Germany.
Bioinformatics. 2020 Dec 30;36(Suppl_2):i643-i650. doi: 10.1093/bioinformatics/btaa790.
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.
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.
The software and the data analyzed are available at https://github.com/SchubertLab/JessEV.
Supplementary data are available at Bioinformatics online.
从概念上讲,基于表位的疫苗设计提出了两个截然不同的问题:(i)选择最佳表位以引发最强的免疫反应,(ii)通过短间隔序列将它们排列和连接成串珠疫苗,以最大化其在抗原处理过程中的回收可能性。当前最先进的方法依次解决了这个设计问题。因此,这些方法无法捕捉到两个设计步骤之间的相互依赖关系,通常强调理论免疫原性而不是正确的疫苗处理,从而导致体内疫苗的免疫原性较低。
在这项工作中,我们提出了一种基于线性规划的计算方法,称为 JessEV,它可以同时解决这两个设计步骤,允许权衡选择具有高免疫原性潜力的一组表位,同时将它们组装成一个串珠结构,以提供高回收几率。我们进行了蒙特卡罗切割模拟,以表明一组固定的表位通常不能被充分组装,而选择适合适当切割要求的表位可以大大提高它们的回收概率,从而使产生的疫苗的有效免疫原性、病原体和人群覆盖率至少提高 2 倍。
软件和分析的数据可在 https://github.com/SchubertLab/JessEV 上获得。
补充数据可在生物信息学在线获得。