Structurally informed evolutionary models improve phylogenetic reconstruction for emerging, seasonal, and pandemic influenza viruses

Abstract

Precise estimation of genetic substitution patterns is critical for accurate reconstruction of pathogen phylogenies. Few studies of viral evolution account for variations of mutation rate across a single gene. This is especially true when considering evolution of segmented viruses where individual segments are short, encoding for few proteins. However, the structural and functional partitions of these proteins could provide valuable information for more accurate inference of viral evolution, due to the disparate immune selection pressure on different functional domains. Accurately reconstructed evolutionary features on specific functional domains can in turn provide biological information on viral protein and immune targets for vaccine design. In this study we developed and evaluated a structurally informed partitioning scheme that accounts for rate variation among immunogenic head and stalk domains of the surface protein hemagglutinin (HA) of influenza viruses. We evaluated the model fit and performance of four different models - HKY, SRD06 codon, HKY with a structurally informed partitioning scheme, SRD06 with a structurally informed partitioning scheme on pandemic A/H1N1pdm09, seasonal A/H1N1postpdm, A/H3N2, B-Yamagata-like and Victoria-like lineages, and two highly pathogenic avian influenza A viruses H5Nx and H7N9. Results showed that structurally informed partitioning with SRD06 performed better for all datasets with decisively statistical support. Significantly faster nucleotide substitution rates for head domain, compared to stalk domain was observed and may provide insight for stalk derived broadly-reactive vaccine design. Taken together, integrating a functionally informed partitioning scheme based on protein structures of immune targets allows for significant improvement of phylogenetic analysis and providing important biological insights.