Figure 3.

TransposeNet is a computational approach that may enable integration of cross-species molecular data to build a more coherent view of proteotoxicity. In this depiction of a recently published study (Khurana et al. 2017), modifiers recovered from genetic screens of proteotoxicity in yeast were “transposed” into the context of the human proteome. (1) Human homologs of yeast modifiers were generated through cross-species consideration of sequence, structure, and protein–protein interactions. (2,3) Genetic and physical interactions (“edges”) between these human genes/protein “nodes” were curated not just from the relatively sparse existing human molecular interaction data sets (2) but also through augmentation of the much richer data set of homologous interactions in the yeast proteome (3). (4) A network was generated to connect the nodes through edges, employing a method known as the Steiner Forest prize-collecting algorithm. The advantage of this method is that it solves a “hairball” of interactions in the most efficient, robust way and introduces “predicted” nodes to do so. The “predicted” nodes can capture biology that was missed in the original screen. In the published example for α-synuclein toxicity, the method captured interactions between many genetic risk factors for Parkinson's disease through specific molecular pathways. It is presumed that the tool can be used to focus attention on specific human genes as potential genetic risk factors, aiding functional genomics efforts.