Coupling Machine Learning with Agent-Based Modeling to Design a Universal Influenza Vaccine

项目摘要/摘要 流感是一种毁灭性的疾病，每年在美国造成多达6.1万人死亡，多达65万人死亡 全球每年的死亡人数。尽管流感疫苗已经存在，但由于序列较快，必须每年修改一次 血凝素(HA)，一种经常被靶向的流感表面蛋白(抗原，Ag)的进化。一种疫苗，用于 对不同的HA序列有效，将大大提高疗效，并将构成 “通用”流感疫苗，可以拯救许多人的生命。为此，在上存在多组残基 血凝素表面不容易变异，因为它们在允许病毒附着和 进入宿主细胞。受体结合位点(RBS)包含这样的“保守”残基，这将是理想的靶点 对于一种通用的流感疫苗；然而，周围可变残基的高度序列多样性 这使得抗体很难与高亲和力的保守残基结合。我们假设一个 通用流感疫苗应包括以HA为基础的AGS的多种免疫接种，并越来越多地 在保守位置周围的不同位置的不同序列。我们希望这种方法能够 为抗体靶向保守的HA残基提供持续的动力 指导他们如何容忍或完全避免与可变残留物结合。为了检验这一假设， 我们将调整我们的亲和力成熟(AM)的计算模型-抗体成熟的过程 体内--致力于将抗HIV抗体进化为针对保守残基的Ag设计的强大工具 苏格兰皇家银行。该模型将包含重要的疾病特征，例如稳定框架的关键作用 抗流感抗体进化中的突变。为了有效地穿越房委会广阔的地貌- 在AGS的基础上，我们将使用深度强化学习(DRL)来引导AM过程朝向最优的AG 序列。我们将首先测试机器学习与随机生物建模的这种独特的耦合 最近开发的AM模型具有粗粒度分辨率，可实现高效的算法优化 参数。然后，我们将把这个框架应用到我们的现实AM模型中，以设计基于真实HA的 通用流感疫苗的序列。优化的HA序列将直接与自然比较 进化的抗流感抗体序列对多种流感具有高效力和中和广度 子类型。

PROJECT SUMMARY/ABSTRACT Influenza is a devasting illness that causes up to 61,000 deaths each year in the United States, and up to 650,000 deaths each year globally. While influenza vaccines exist, they must be modified annually due to rapid sequence evolution of hemagglutinin (HA), the oft-targeted influenza surface protein (antigen, Ag). A vaccine formulated to be effective against diverse HA sequences would have greatly increased efficacy and would constitute a ‘universal’ influenza vaccine that could save many lives. To this end, there exist multiple groups of residues on the surface of HA that do not mutate as readily, due to their functional role in allowing the virus to attach to and enter host cells. The receptor binding site (RBS) contains such ‘conserved’ residues, which would be ideal targets for a universal influenza vaccine; however, the high sequence diversity of the surrounding ‘variable’ residues renders it difficult for antibodies (Abs) to bind to the conserved residues with high affinity. We hypothesize a universal influenza vaccine should comprise multiple immunizations of HA-based Ags with increasingly diverse sequences at variable positions surrounding conserved sites. We expect this approach to provide a continuous driving force for Abs to target conserved HA residues while simultaneously coaching them on how to tolerate or altogether avoid binding to variable residues. To test this hypothesis, we will adapt our computational model of affinity maturation (AM) – the process by which antibodies mature in vivo – geared towards evolving anti-HIV Abs, into a robust tool for Ag design against the conserved residues of the RBS. This model will incorporate important disease features, such as the crucial role of stabilizing framework mutations in the evolution of anti-influenza Abs. To efficiently traverse the vast sequence landscape of the HA- based Ags, we will employ deep reinforcement learning (DRL) to steer the AM process towards the optimal Ag sequences. We will first test this unique coupling of machine learning with stochastic biological modeling on our recently developed AM model with coarse-grained resolution to enable efficient optimization of algorithmic parameters. We will then apply this framework to our realistic AM model towards the design of real HA-based sequences for a universal influenza vaccine. Optimized HA sequences will be directly compared against naturally evolved anti-influenza Ab sequences with high potency and neutralization breadth against multiple influenza subtypes.