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
基本信息
- 批准号:10444310
- 负责人:
- 金额:$ 26.48万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AffinityAlgorithmsAmino Acid SequenceAntibodiesAntibody ResponseAntigensB-cell receptor repertoire sequencingBase SequenceBindingBinding SitesBiological ModelsCellsCessation of lifeChemicalsCommunicable DiseasesCommunity HealthComputational ScienceComputational algorithmComputer ModelsCoupledCouplingData ScienceDiseaseEconomic BurdenEngineeringEnvironmentEvolutionFramework RegionsFutureGeneticGoalsGrainHIVHIV AntibodiesHealthHemagglutininHumanImmune responseImmune systemImmunizationInfluenzaLearningMachine LearningMalariaMembrane ProteinsMethodologyModelingMutationNatureNucleotidesOutputPathogenicityPerformancePositioning AttributeProcessProteinsPublic HealthPunishmentResolutionRewardsRoleRunningSavingsSiteSpeedSurfaceSystemTestingUnited StatesVaccinationVaccinesVirusanti-influenzaantigen antibody bindingbaseburden of illnesscomputational pipelinescross reactivitydeep reinforcement learningdeep sequencingdesigndriving forcein silicoin vivoinfluenza virus straininfluenza virus vaccineinsightlearning algorithmmachine learning algorithmneutralizing antibodynext generationnovelnovel strategiespathogenrational designreceptor bindingresponsesimulationtooluniversal influenza vaccineuniversal vaccinevaccination strategyvaccine candidate
项目摘要
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。
项目成果
期刊论文数量(0)
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{{ truncateString('Kayla Sprenger', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 26.48万 - 项目类别:
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