ConvMut: Exploration of viral convergent mutations along phylogenies

Abstract

Convergent evolution in protein antigens is common across pathogens and has also been documented in SARS-CoV-2 (hCoV-19); the most likely reason is the need to evade the selective pressure exerted by previous infection- or vaccine-elicited immunity. There is a pressing need for tools that allow automated analysis of convergent mutations. In response to this need, we developed ConvMut, a tool to analyze genetic sequence data to identify patterns of recurrent mutations in SARS-CoV-2 evolution. To this end, we exploited the granular phylogenetic tree representation developed by PANGO, allowing us to observe what we call deltas, i.e., groups of mutations that are acquired on top of the immediately upstream tree nodes. Deltas comprise amino acid substitutions, insertions, and deletions. ConvMut can perform individual protein analysis to identify the most common single mutations acquired independently in a given subtree (starting from a user-selected root). Such mutations are represented in a barplot that can be sorted by frequency or position, and filtered by region of interest. Lineages are then gathered into clusters according to their sets of shared mutations. Finally, an interactive graph orders the evolutionary steps of clusters, details the acquired amino acid changes for each sublineage, and allows us to trace the evolutionary path until a selected lineage. Other unique tools are paired with the main functionality of ConvMut to support a complete analysis, such as a frequency analysis for a given nucleotide or amino acid changes at a given residue across a selected phylogenetic subtree. ConvMut will facilitate the design of antiviral anti-Spike monoclonal antibodies and Spike-based vaccines with longer-lasting efficacy, minimizing development and marketing failures.