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: 10619595
• 负责人: Kayla Sprenger
• 金额: $ 14.71万
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10619595
• 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; Site; Speed; Surface; System; Testing; United States; Vaccination; Vaccines; Virus; anti-influenza; antibody and antigen binding; 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; nonbinary; 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.

项目摘要/摘要 流感是一种破坏性的疾病，在美国每年最多可导致61,000人死亡，高达65万 每年全球死亡。虽然存在影响疫苗，但由于序列快速，必须每年修改它们 血凝素（HA）的进化，即靶向数量的影响力表面蛋白（抗原，Ag）。配制的疫苗 对不同的HA序列有效，将大大提高效率，并且将构成 “普遍”影响可以挽救许多生命的疫苗。为此，有多个救援 HA表面不容易突变，因为它们在允许病毒附着的功能作用和 输入宿主单元。接收器结合位点（RB）包含这种“保守”保留，这将是理想的目标 普遍影响疫苗；但是，周围“变量”残差的高序列多样性 使抗体（ABS）很难与高亲和力结合到配置的保留率。我们假设一个 通用影响者疫苗应越来越多地完成基于HA的AG的多次免疫 在组成位点周围可变位置处的不同序列。我们期望这种方法 提供持续的驱动力，以使ABS同时配置为目标HA恢复目标 指导他们如何忍受或完全避免与可变保留率约束。为了检验这一假设， 我们将适应亲和力成熟的计算模型（AM） - 抗体成熟的过程 体内 - 旨在将抗HIV ABS进化为一种坚固的工具，用于AG设计，以防止保守的保留。 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 抗炎性ABS进化的突变。有效地遍历了Ha-的巨大序列景观 基于AGS，我们将采用深度强化学习（DRL）来窃取AM过程 序列。我们将首先在我们的机器学习上测试这种机器学习的独特耦合 最近开发了具有粗粒分辨率的AM模型，以实现算法的有效优化 参数。然后，我们将将此框架应用于我们现实的AM模型 通用流感疫苗的序列。优化的HA序列将直接与自然进行比较 具有高效力和中和宽度的进化的抗激素AB序列与多重影响 亚型。