美国麻省理工学院Bryan Bryson、Bonnie Berger等研究人员合作揭示病毒进化和逃逸的模式。该项研究成果发表在2021年1月15日出版的《科学》杂志上。

研究人员使用原本为人类自然语言开发的机器学习算法对病毒逃逸进行建模。研究人员将逃逸突变鉴定为保留病毒感染性但导致病毒看上去与免疫系统不同的突变，这类似于保留句子语法但改变其含义的单词更改。通过这种方法，流感血凝素、HIV-1包膜糖蛋白（HIV Env）和严重急性呼吸系统综合症冠状病毒2（SARS-CoV-2）突刺病毒蛋白的语言模型可以单独使用序列数据来准确地预测结构逃逸模式。这项研究可能是自然语言与病毒进化之间的概念桥梁。

据悉，病毒发生变异并逃避人类免疫系统，从而引起感染（被称为病毒逃逸），这仍然是抗病毒和疫苗开发的障碍。了解控制逃生的复杂规则可能有助于疗法开发。

附：英文原文

Title: Learning the language of viral evolution and escape

Author: Brian Hie, Ellen D. Zhong, Bonnie Berger, Bryan Bryson

Issue&Volume: 2021/01/15

Abstract: The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentence’s grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution.

DOI: 10.1126/science.abd7331

Source: https://science.sciencemag.org/content/371/6526/284