Learning the language of viral evolution and escape

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

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)284-288
Number of pages5
JournalScience
Volume371
Issue number6526
DOIs
StatePublished - Jan 15 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General

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