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Learning the language of viral evolution and escape.
Hie, Brian; Zhong, Ellen D; Berger, Bonnie; Bryson, Bryan.
  • Hie B; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Zhong ED; Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA 02139, USA.
  • Berger B; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Bryson B; Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Science ; 371(6526): 284-288, 2021 01 15.
Article in English | MEDLINE | ID: covidwho-1033401
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza A virus / Acquired Immunodeficiency Syndrome / HIV-1 / Influenza, Human / SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Science Year: 2021 Document Type: Article Affiliation country: Science.abd7331

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Influenza A virus / Acquired Immunodeficiency Syndrome / HIV-1 / Influenza, Human / SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Vaccines Limits: Humans Language: English Journal: Science Year: 2021 Document Type: Article Affiliation country: Science.abd7331