Our paper “Sub-dominant principal components inform new vaccine targets for HIV Gag” with Faraz, Ahmed and Matthew (HKUST) has just appeared in Bioinformatics.
The paper proposes a computational method to accurately infer networks of interacting sites in viral proteins such as HIV Gag from patient-derived data sequences. We reveal that certain networks that appear important for vaccine design purposes are not accurately reflected by previous methods, based only on the dominant PCs. Rather, these networks are encoded jointly by both dominant and sub-dominant PCs. The new method is able to identify a network of interacting sites of HIV Gag that associated very strongly with viral control. Based on this, several new candidates for a potent T-cell-based HIV vaccine are put forward.