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

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Abstract
Summary: 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.
Funding Information
  • National Institutes of Health (HL068117)