从患者数据推断的自旋模型忠实地描述了HIV适应性景观，并实现了合理的疫苗设计
Spin models inferred from patient data faithfully describe HIV fitness landscapes and enable rational vaccine design
摘要：
疫苗诱导的免疫应答的突变逃逸已经阻碍了针对AIDS的成功疫苗的开发，AIDS的病原体是HIV，一种高度可变的病毒。了解病毒的适应性作为其蛋白质组序列的函数可以使有效疫苗的合理设计成为可能，因为该信息可以将疫苗诱导的免疫应答集中于病毒的靶向突变脆弱性。自旋模型已被提出作为一种手段来推断从患者衍生的病毒蛋白质序列的HIV蛋白质的内在适应度景观。这些序列是由患者特异性免疫反应驱动的非平衡病毒进化的产物，并且受到系统发育限制。这样的序列数据如何允许推断内在适应度景观？我们结合了计算机模拟和费曼变分理论，表明在大多数情况下，从患者来源的病毒序列推断的自旋模型反映了突变病毒株适应性的正确等级顺序。我们的发现与多种病毒有关。
英文摘要：
Mutational escape from vaccine induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of non-equilibrium viral evolution driven by patient-specific immune responses, and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory á la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses.
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