Learning the language of viral evolution and escape

doi:10.1126/science.abd7331

GSTDTAP > 气候变化

DOI	10.1126/science.abd7331
	Learning the language of viral evolution and escape
	Brian Hie; Ellen D. Zhong; Bonnie Berger; Bryan Bryson
	2021-01-15
发表期刊	Science
出版年	2021
英文摘要	Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. Science , this issue p. [284][1]; see also p. [233][2] 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. [1]: /lookup/doi/10.1126/science.abd7331 [2]: /lookup/doi/10.1126/science.abf6894
领域	气候变化 ; 资源环境
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/311506
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Brian Hie,Ellen D. Zhong,Bonnie Berger,et al. Learning the language of viral evolution and escape[J]. Science,2021.
APA	Brian Hie,Ellen D. Zhong,Bonnie Berger,&Bryan Bryson.(2021).Learning the language of viral evolution and escape.Science.
MLA	Brian Hie,et al."Learning the language of viral evolution and escape".Science (2021).