Yu Timothy C, Thornton Zorian T, Hannon William W, DeWitt William S, Radford Caelan E, Matsen Frederick A, Bloom Jesse D
Basic Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.
Computational Biology Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.
Virus Evol. 2022 Dec 12;8(2):veac110. doi: 10.1093/ve/veac110. eCollection 2022.
A challenge in studying viral immune escape is determining how mutations combine to escape polyclonal antibodies, which can potentially target multiple distinct viral epitopes. Here we introduce a biophysical model of this process that partitions the total polyclonal antibody activity by epitope and then quantifies how each viral mutation affects the antibody activity against each epitope. We develop software that can use deep mutational scanning data to infer these properties for polyclonal antibody mixtures. We validate this software using a computationally simulated deep mutational scanning experiment and demonstrate that it enables the prediction of escape by arbitrary combinations of mutations. The software described in this paper is available at https://jbloomlab.github.io/polyclonal.
研究病毒免疫逃逸的一个挑战在于确定突变如何组合以逃避多克隆抗体，因为多克隆抗体可能潜在地靶向多个不同的病毒表位。在此，我们引入了该过程的生物物理模型，该模型按表位划分总多克隆抗体活性，然后量化每个病毒突变如何影响针对每个表位的抗体活性。我们开发了一款软件，它可以利用深度突变扫描数据来推断多克隆抗体混合物的这些特性。我们通过计算模拟的深度突变扫描实验验证了该软件，并证明它能够预测由任意突变组合导致的逃逸。本文所述软件可在https://jbloomlab.github.io/polyclonal获取。
