Greaney Allison J, Starr Tyler N, Bloom Jesse D
Basic Sciences and Computational Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
bioRxiv. 2021 Dec 7:2021.12.04.471236. doi: 10.1101/2021.12.04.471236.
A key goal of SARS-CoV-2 surveillance is to rapidly identify viral variants with mutations that reduce neutralization by polyclonal antibodies elicited by vaccination or infection. Unfortunately, direct experimental characterization of new viral variants lags their sequence-based identification. Here we help address this challenge by aggregating deep mutational scanning data into an "escape calculator" that estimates the antigenic effects of arbitrary combinations of mutations to the virus's spike receptor-binding domain (RBD). The calculator can be used to intuitively visualize how mutations impact polyclonal antibody recognition, and score the expected antigenic effect of combinations of mutations. These scores correlate with neutralization assays performed on SARS-CoV-2 variants, and emphasize the ominous antigenic properties of the recently described Omicron variant. An interactive version of the calculator is at https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ , and we provide a Python module for batch processing.
新型冠状病毒2(SARS-CoV-2)监测的一个关键目标是快速识别出带有能降低疫苗接种或感染所引发的多克隆抗体中和作用的突变的病毒变体。不幸的是,对新病毒变体的直接实验表征滞后于基于序列的识别。在此,我们通过将深度突变扫描数据整合到一个“逃逸计算器”中来应对这一挑战,该计算器可估计病毒刺突受体结合域(RBD)的任意突变组合的抗原效应。该计算器可用于直观地可视化突变如何影响多克隆抗体识别,并对突变组合的预期抗原效应进行评分。这些评分与对SARS-CoV-2变体进行的中和试验相关,并突出了最近描述的奥密克戎变体不祥的抗原特性。该计算器的交互式版本位于https://jbloomlab.github.io/SARS2_RBD_Ab_escape_maps/escape-calc/ ,我们还提供了一个用于批量处理的Python模块。