The antibody repertoire possesses near-limitless diversity, enabling the adaptive immune system to accommodate essentially any antigen. However, this diversity explores the antigenic space unequally, allowing some pathogens like influenza virus to impose complex immunodominance hierarchies that distract antibody responses away from key sites of virus vulnerability. We developed a computational model of affinity maturation to map the patterns of immunodominance that evolve upon immunization with natural and engineered displays of hemagglutinin (HA), the influenza vaccine antigen. Based on this knowledge, we designed immunization protocols that subvert immune distraction and focus serum antibody responses upon a functionally conserved, but immunologically recessive, target of human broadly neutralizing antibodies. We tested in silico predictions by vaccinating transgenic mice in which antibody diversity was humanized to mirror clinically relevant humoral output. Collectively, our results demonstrate that complex patterns in antibody immunogenicity can be rationally defined and then manipulated to elicit engineered immunity.

抗体库具有近乎无限的多样性，使适应性免疫系统基本上可以适应任何抗原。然而，这种多样性对抗原空间的探索并不均衡，使得流感病毒等一些病原体能够施加复杂的免疫优势层次结构，从而将抗体反应从病毒脆弱性的关键部位转移开。我们开发了一种亲和力成熟的计算模型，以绘制免疫优势模式，该模式在使用天然和工程化显示的血凝素 (HA)（流感疫苗抗原）进行免疫后进化。基于这一知识，我们设计了免疫方案，颠覆免疫干扰并将血清抗体反应集中在功能保守但免疫隐性的人类广泛中和抗体的目标上。我们测试了通过给转基因小鼠接种疫苗来进行计算机模拟预测，其中抗体多样性被人性化以反映临床相关的体液输出。总的来说，我们的结果表明，可以合理定义抗体免疫原性的复杂模式，然后对其进行操作以引发工程免疫。