Learning the language of viral evolution and escape
This study modeled viral escape with machine learning algorithms originally developed for human natural language, and 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.
Abstract
<jats:title>Natural language predicts viral escape</jats:title> <jats:p> 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 <jats:italic>et al.</jats:italic> 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. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6526" page="284" related-article-type="in-this-issue" vol="371" xlink:href="10.1126/science.abd7331">284</jats:related-article> ; see also p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" issue="6526" page="233" related-article-type="in-this-issue" vol="371" xlink:href="10.1126/science.abf6894">233</jats:related-article> </jats:p>