title: Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein-Protein Interactions.
abstract: Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.
authors.affiliation: From the ‡Department of Biochemistry and Molecular Biology.|§Michael Smith Laboratories.|¶Centre for Blood Research.
authors.forename: Nikolaus
authors.identifier: 0000-0003-4025-9968
authors.initials: N
authors.lastname: Fortelny
authors.affiliation: ¶Centre for Blood Research.|‖Department of Oral Biological and Medical Sciences, Faculty of Dentistry.
authors.forename: Georgina S
authors.initials: GS
authors.lastname: Butler
authors.affiliation: From the ‡Department of Biochemistry and Molecular Biology.|¶Centre for Blood Research.|‖Department of Oral Biological and Medical Sciences, Faculty of Dentistry.
authors.forename: Christopher M
authors.identifier: 0000-0001-5844-2731
authors.initials: CM
authors.lastname: Overall
authors.affiliation: §Michael Smith Laboratories; paul@chibi.ubc.ca.|**Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Paul
authors.identifier: 0000-0002-0426-5028
authors.initials: P
authors.lastname: Pavlidis
mesh_terms: D006801:Humans; D000069550:Machine Learning; D010447:Peptide Hydrolases / Q000378:metabolism*; D010802:Phylogeny; D011480:Protease Inhibitors / Q000378:metabolism*; D025941:Protein Interaction Mapping
chemical_list: D011480:Protease Inhibitors; D010447:Peptide Hydrolases