ARNLE model identifies prevalence potential of SARS-CoV-2 variants

Abstract

SARS-CoV-2 mutations accumulated during the COVID-19 pandemic, posing significant challenges for immune prevention. An optimistic perspective suggests that SARS-CoV-2 will become more tropic to humans with weaker virulence and stronger infectivity. However, tracing a quantified trajectory of this process remains difficult. Here we introduce an attentional recurrent network based on language embedding (ARNLE) framework to analyse the shift in SARS-CoV-2 host tropism towards humans. ARNLE incorporates a language model for self-supervised learning to capture the features of amino acid sequences, alongside a supervised bidirectional long-short-term-memory-based network to discern the relationship between mutations and host tropism among coronaviruses. We identified a shift in SARS-CoV-2 tropism from weak to strong, transitioning from an approximate Chiroptera coronavirus to a primate-tropic coronavirus. Delta variants were closer to other common primate coronaviruses than previous SARS-CoV-2 variants. A similar phenomenon was observed among the Omicron variants. We employed a Bayesian-based post hoc explanation method to analyse key mutations influencing the human tropism of SARS-CoV-2. ARNLE identified pivotal mutations in the spike proteins, including T478K, L452R, G142D and so on, as the top determinants of human tropism. Our findings suggest that language models like ARNLE will significantly facilitate the identification of potentially prevalent variants and provide important support for screening key mutations, aiding in timely update of vaccines to protect against future emerging SARS-CoV-2 variants.