Using Evolved Neural Networks to Elucidate Nef Features Associated with HIV-1 Subtype Differentiation

Abstract

The genetically diverse HIV-1 Group M infecting subtypes can be observed as unique branches on a phylogenetic tree and arose due to independent cross-species transmissions between non-human primates and humans. As the HIV-1 pandemic has evolved, different infecting subtypes have prevailed in different geographic populations. The complex factors associated with the global establishment of specific subtypes remains largely unknown. The HIV-1 accessory protein Nef, demonstrates considerable genetic variability and several studies suggest that Nef variation is associated with disease progression. Here we use an evolved neural network approach applied to a well-curated database of HIV-1 Nef sequences from subtypes A1, C, and D, the most prominent subtypes in Uganda, Africa to elucidate functional properties associated with subtype diversity. Following the generation of over 1000 features associated with amino acids physicochemical properties, we use statistical pruning and evolved neural networks to identify key Nef features associated with subtype differentiation. As interest in Nef continues to grow in the research community, we hope that these features foster new understanding of the mechanisms associated with the spread of HIV -1 subtypes in populations.