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

将机器学习与基于代理的建模相结合来设计通用流感疫苗

• 批准号: 10444310
• 负责人: Kayla Sprenger
• 金额: $ 26.48万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-09 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10444310
• 关键词: Affinity; Algorithms; Amino Acid Sequence; Antibodies; Antibody Response; Antigens; B-cell receptor repertoire sequencing; Base Sequence; Binding; Binding Sites; Biological Models; Cells; Cessation of life; Chemicals; Communicable Diseases; Community Health; Computational Science; Computational algorithm; Computer Models; Coupled; Coupling; Data Science; Disease; Economic Burden; Engineering; Environment; Evolution; Framework Regions; Future; Genetic; Goals; Grain; HIV; HIV Antibodies; Health; Hemagglutinin; Human; Immune response; Immune system; Immunization; Influenza; Learning; Machine Learning; Malaria; Membrane Proteins; Methodology; Modeling; Mutation; Nature; Nucleotides; Output; Pathogenicity; Performance; Positioning Attribute; Process; Proteins; Public Health; Punishment; Resolution; Rewards; Role; Running; Savings; Site; Speed; Surface; System; Testing; United States; Vaccination; Vaccines; Virus; anti-influenza; antigen antibody binding; base; burden of illness; computational pipelines; cross reactivity; deep reinforcement learning; deep sequencing; design; driving force; in silico; in vivo; influenza virus strain; influenza virus vaccine; insight; learning algorithm; machine learning algorithm; neutralizing antibody; next generation; novel; novel strategies; pathogen; rational design; receptor binding; response; simulation; tool; universal influenza vaccine; universal vaccine; vaccination strategy; vaccine candidate

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.

项目总结/摘要 流感是一种毁灭性的疾病，每年在美国造成多达61，000人死亡， 全球每年的死亡人数。虽然存在流感疫苗，但由于快速测序，它们必须每年进行修改 血凝素（HA）的演变，经常靶向流感表面蛋白（抗原，Ag）。一种疫苗， 有效对抗不同的HA序列将具有大大增加的功效，并将构成一种新的治疗方法。 “通用”流感疫苗可以挽救许多生命。为此，上存在多组残基 HA的表面不容易突变，因为它们的功能作用是允许病毒附着并 进入宿主细胞。受体结合位点（RBS）含有这样的“保守”残基，这将是理想的靶点 对于通用流感疫苗;然而，周围“可变”残基的高度序列多样性 使得抗体（Ab）难以以高亲和力结合保守残基。我们假设 通用流感疫苗应包括基于HA的Ag的多次免疫， 在保守位点周围的可变位置上的不同序列。我们希望这种方法能够 为Ab靶向保守的HA残基提供持续的驱动力，同时 指导他们如何容忍或完全避免与可变残基结合。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-面向发展抗HIV抗体，成为针对保守残基的Ag设计的强大工具 RBS。该模型将纳入重要的疾病特征，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HA的巨大序列景观- 基于Ags，我们将采用深度强化学习（DRL）将AM过程转向最佳Ag 序列的我们将首先测试机器学习与随机生物建模的这种独特耦合， 最近开发的具有粗粒度分辨率的AM模型，以实现算法的有效优化 参数然后，我们将应用这个框架，我们的现实AM模型对设计的真实的HA为基础的 通用流感疫苗的序列。优化的HA序列将直接与天然HA序列进行比较。 进化的抗流感抗体序列具有针对多种流感的高效力和中和宽度 亚型。