Learning the language of viral evolution and escape

By Brian L. Hie, Ellen D. Zhong, Bonnie Berger, Bryan D. Bryson. 2020

317 citations recorded by the source corpus.

Natural language predicts viral escape 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; see also p. 233 Language models of influenza hemagglutinin, HIV Env, and SARS-CoV-2 spike viral protein sequences can accurately predict viral escape patterns. 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.

Subfields: Molecular Biology

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Citations in the corpus

ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction (2021, unknown): “[98] B.”
Early computational detection of potential high-risk SARS-CoV-2 variants (2021, background): “In particular, the log-likelihood metric supports substitutions, insertions, and deletions without requiring a reference sequence to measure against (unlike the grammaticality of Hie et al. [37]).”
Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, background): “…protein MLMs contain structural information (Rao et al., 2019; Rives et al., 2021; Chowdhury et al., 2021), encode evolutionary trajectories (Hie et al., 2022b; 2021), are zero-shot predictors of mutation fitness effects (Meier et al., 2021), improve out-of-domain generalization on protein…”
The language of proteins: NLP, machine learning & protein sequences (2021, background): “Language models have been used to understand and predict viral mutations that evade neutralizing antibodies [35].”
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics (2022, background): “Recently, Hie et al. (Hie et al., 2021) used protein language models (PLMs) and adapted concepts from NLP to model escape variants across three different viruses, including SARS-CoV-2.”
Exploring evolution-based & -free protein language models as protein function predictors (2022, methodology): “In this paper, we focus on functional annotation prediction (classiﬁcation task) [45, 5] and ﬁtness prediction (including both regression [8, 25] and zero-shot prediction [16, 24] tasks), as shown in Figure 2.”
Comprehensive phylogenetic analysis of the ribonucleotide reductase family reveals an ancestral clade (2022, unknown): “The search result was visualized as a force-directed graph network in Cytoscape ( Hie et al., 2021 ; Shannon et al., 2003 ).”
Evolutionary velocity with protein language models (2021, unknown): “Promising advances in machine learning have improved the ability of a class of algorithms called language models to learn the rules that govern how amino acids can appear together to form a protein sequence (Alley et al., 2019; Bepler and Berger, 2019, 2021; Hie et al., 2021; Hsu et al., 2022; Rao e”