Abstract
Knowledge of specific domain-domain interactions (DDIs) is essential to understand the functional significance of protein interaction networks. Despite the availability of an enormous amount of data on protein-protein interactions (PPIs), very little is known about specific DDIs occurring in them. Here, we present a top-down approach to accurately infer functionally relevant DDIs from PPI data. We created a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs by combining datasets from five major interaction databases. We introduced an integrated scoring system that uses a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting domains, which can map the interacting propensity of two domains in many dimensions. This method outperforms similar existing methods both in the accuracy of prediction and in the coverage of domain interaction space. We predicted a set of 52,492 high-confidence DDIs to carry out cross-species comparison of DDI conservation in eight model species including human, mouse, Drosophila, C. elegans, yeast, Plasmodium, E. coli and Arabidopsis. Our results show that only 23% of these DDIs are conserved in at least two species and only 3.8% in at least 4 species, indicating a rather low conservation across species. Pair-wise analysis of DDI conservation revealed a ‘sliding conservation’ pattern between the evolutionarily neighboring species. Our methodology and the high-confidence DDI predictions generated in this study can help to better understand the functional significance of PPIs at the modular level, thus can significantly impact further experimental investigations in systems biology research.
Highlights
Proteins rarely function alone; a vast majority of proteins must interact with other proteins to perform their intended functions
Method development The comprehensive, non-redundant dataset of protein-protein interactions (PPIs) compiled by us in this work contains 209,165 PPIs that has an extensive coverage of domain space
Experimentally-determined datasets representing non-interacting protein pairs are virtually lacking; limiting the development of negative models by computational methods to compare against the positive datasets
Summary
Proteins rarely function alone; a vast majority of proteins must interact with other proteins to perform their intended functions. The binding interface of the interaction is generally localized to specific conserved segments of the interacting proteins that are broadly known as domains. These domains create the interface of an interaction through highly specific recognition events. Knowledge on domain-domain interactions (referred to as DDIs) is very important for understanding the nature and the significance of PPIs. For instance, DDIs have been used to gain a better understanding of protein networks [2], for predicting the effects of mutations [3] and alternative splicing events that effect interacting domains [4], for developing drugs to inhibit pathological protein interactions [5,6], and for designing novel protein interactions [7]
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