Enhanced detection and molecular modeling of adaptive mutations in SARS-CoV-2 coding and non-coding regions using the...

Area of Science:

• Virology
• Molecular Evolution
• Genomics

Background:

• Identifying beneficial mutations in viral genomes is key to understanding molecular evolution and pathogenicity.
• Traditional methods like Ka/Ks assume synonymous sites are neutral, which can lead to inaccurate predictions of adaptive mutations.
• Synonymous sites in translated regions (TRs) and untranslated regions (UTRs) can be under selection, violating neutrality assumptions.

Purpose of the Study:

• To refine the relative substitution rate (c/µ) test for identifying adaptive mutations in viral genomes.
• To improve the accuracy of detecting beneficial selection without relying on the neutrality assumption of synonymous sites.
• To identify specific nucleotide and amino acid sites under beneficial selection in viral UTRs and TRs.

Main Methods:

• Optimized the mutation rate (µ) value within the c/µ test.
• Applied the refined c/µ test to identify sites under beneficial selection in viral UTRs and TRs.
• Utilized molecular modeling to elucidate the adaptive mechanisms of identified mutations in critical viral proteins.

Main Results:

• Identified 11 nucleotide sites in UTRs with a c/µ > 3.
• Detected 69 nonsynonymous sites (c/µ > 3 and Ka/Ks > 2.5) and 107 synonymous sites (Ks/µ > 3) in TRs under beneficial selection.
• Found that top identified mutations in UTRs and TRs have reported or predicted functional effects.

Conclusions:

• The refined c/µ test provides a more accurate method for detecting adaptive mutations in viral genomes.
• The study identified specific sites under beneficial selection, contributing to a deeper understanding of viral adaptation.
• Molecular modeling offers insights into the functional consequences of adaptive mutations in key viral proteins.