Sub-dominant principal components inform new vaccine targets for HIV Gag

Syed Faraz Ahmed, Ahmed A Quadeer, David Morales-Jimenez, Matthew R. McKay

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Abstract

Motivation

Patterns of mutational correlations, learnt from patient-derived sequences of human immunodeficiency virus (HIV) proteins, are informative of biochemically linked networks of interacting sites that may enable viral escape from the host immune system. Accurate identification of these networks is important for rationally designing vaccines which can effectively block immune escape pathways. Previous computational methods have partly identified such networks by examining the principal components (PCs) of the mutational correlation matrix of HIV Gag proteins. However, driven by a conservative approach, these methods analyze the few dominant (strongest) PCs, potentially missing information embedded within the sub-dominant (relatively weaker) ones that may be important for vaccine design.

Results

By using sequence data for HIV Gag, complemented by model-based simulations, we revealed that certain networks of interacting sites that appear important for vaccine design purposes are not accurately reflected by the dominant PCs. Rather, these networks are encoded jointly by both dominant and sub-dominant PCs. By incorporating information from the sub-dominant PCs, we identified a network of interacting sites of HIV Gag that associated very strongly with viral control. Based on this network, we propose several new candidates for a potent T-cell-based HIV vaccine.
Original languageEnglish
Pages (from-to)3884–3889
Number of pages6
JournalBioinformatics
Volume35
Issue number20
Early online date28 Jun 2019
DOIs
Publication statusPublished - 15 Oct 2019

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