A data-mining approach to rank candidate protein-binding partners-The case of biogenesis of lysosome-related organelles complex-1 (BLOC-1)

The study of protein-protein interactions is a powerful approach to uncovering the molecular function of gene products associated with human disease. Protein-protein interaction data are accumulating at an unprecedented pace owing to interactomics projects, although it has been recognized that a significant fraction of these data likely represents false positives. During our studies of biogenesis of lysosome-related organelles complex-1 (BLOC-1), a protein complex involved in protein trafficking and containing the products of genes mutated in Hermansky-Pudlak syndrome, we faced the problem of having too many candidate binding partners to pursue experimentally. In this work, we have explored ways of efficiently gathering high-quality information about candidate binding partners and presenting the information in a visually friendly manner. We applied the approach to rank 70 candidate binding partners of human BLOC-1 and 102 candidates of its counterpart from Drosophila melanogaster. The top candidate for human BLOC-1 was the small GTPase encoded by the RAB11A gene, which is a paralogue of the Rab38 and Rab32 proteins in mammals and the lightoid gene product in flies. Interestingly, genetic analyses in D. melanogaster uncovered a synthetic sick/lethal interaction between Rab11 and lightoid. The data-mining approach described herein can be customized to study candidate binding partners for other proteins or possibly candidates derived from other types of 'omics' data.