Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data
Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data
复制标题
从时间顺序序列数据推断流感样进化抗原的内在突变适应性景观
DOI：
10.1103/physreve.105.024401
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发表时间：
2022
影响因子：
2.4
通讯作者：
Chakraborty, Arup K.
中科院分区：
文献类型：
--
作者：
Doelger, Julia;Kardar, Mehran;Chakraborty, Arup K.
对于在免疫压力下不断进化的病毒，如季节性流感，仍然没有有效的长期保护性疫苗，季节性流感已经在人群中造成并可能造成毁灭性的流行病。为了找到这样一种广泛的保护性免疫策略，了解病毒通过突变逃避特异性抗体反应的容易程度是有用的。该信息被编码在病毒蛋白质的适应度景观中（即，作为序列函数的病毒适应性的知识）。在这里，我们提出了一种计算方法来推断流感样进化抗原的内在突变适应度景观从每年的序列数据。我们测试的推理性能与计算机生成的序列数据的基础上随机模拟模仿免疫驱动的病毒进化的基本特征。虽然数值模拟模型确实基于允许的突变创建了一个遗传学，但推理方案并不使用此信息。这与依赖于系统发育树重建的其他方法形成对比。我们的方法只需要足够数量的样本超过多年。使用我们的方法，我们能够从短抗原蛋白的模拟序列时间序列推断单个以及成对突变适应性效应。我们的健身推理方法可能有潜在的未来使用的免疫方案的设计，通过识别固有的脆弱的免疫目标组合的抗原，免疫驱动的选择下演变。将来，这种方法可能会应用于流感和其他新型病毒，如SARS-CoV-2，它会进化，并且像流感一样，可能会继续逃避自然和疫苗介导的免疫压力。
There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. To find such a broadly protective immunization strategy, it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenzalike evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method, we are able to infer single as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for the design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. In the future, this approach may be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.