Key result
Computational modeling of SARS-CoV-2 shows synonymous mutations are nearly neutral and stop-codons are deleterious.
Why the study?
Experimentally measuring the fitness effects of mutations to SARS-CoV-2 proteins is challenging due to lack of tractable lab assays and limited deep mutational scanning data.
A novel computational framework leverages millions of SARS-CoV-2 sequences to estimate the fitness effects of viral mutations, providing a tool to assess new variants and therapeutic targets.
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May guide variant surveillance; leaves open therapeutic translation pending validation.
Bloom et al. (2023) studied SARS-CoV-2. Computational estimation of mutation fitness effects vs. Expected observations in the absence of selection was evaluated on Fitness effects of mutations. A computational approach leveraging millions of SARS-CoV-2 sequences estimated mutation fitness effects, showing synonymous mutations are nearly neutral and stop-codon mutations are deleterious.
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