Abstract
Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins. Specifically, by introducing a robust statistical method based on sparse principal component analysis, we identify near-disjoint sets of collectively-correlated residues (sectors) having mostly a one-to-one association to largely distinct structural or functional domains. This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution, with each mode involving a sparse and localized network of mutational interactions. Moreover, individual inferred sectors of HIV are shown to carry immunological significance, providing insight for guiding targeted vaccine strategies.
Highlights
Human Immunodeficiency Virus (HIV) and Hepatitis C Virus (HCV) are the cause of devastating infectious diseases that continue to wreak havoc worldwide
By employing available sequence data, we investigated the co-evolutionary interaction networks for multiple HIV and HCV proteins
Our proposed approach robust co-evolutionary analysis (RoCA) resolves co-evolutionary structures by applying a spectral analysis to the mutational (Pearson) correlation matrices and identifying inherent structure embedded within the principal components (PCs), which are representative of strong modes of correlation in the data
Summary
HIV and HCV are the cause of devastating infectious diseases that continue to wreak havoc worldwide. For HIV, recent analytical, numerical and experimental studies [4,5,6,7] provide support for this premise, indicating that these patterns may be seen as population-averaged evolutionary “footprints” of viral escape during the host-pathogen combat in individual patients. This idea has been applied to propose quantitative fitness landscapes for both HIV [8, 9] and HCV [10] which are predictive of relative viral fitness, as verified through experimentation and clinical data
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