Learning the language of viral evolution and escape.
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.
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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