Protein-protein interfaces integrated into interaction networks: implications on drug design.
ABSTRACT The growing perception that diseases are often consequences of multiple molecular abnormalities rather than being the result of a single defect highlights the importance of network-centric view in therapeutic approaches. Protein interaction networks may contribute to understanding of disease, assist in drug design and discovery. Here, we review some recent advances in disease-associated protein interaction networks taking a structural approach. We first describe structural aspects of protein-protein interactions and properties of protein interfaces as related to drug design; we address protein interactions in a network perspective; in particular, we illustrate how integrating protein interfaces onto interaction networks can guide the identification of selective drug targets or drugs targeting multiple proteins in a network.
- SourceAvailable from: Vasant Honavar[show abstract] [hide abstract]
ABSTRACT: Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH) with one or two binding sites, or multiple-interface hubs (MIH) with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations) or party hubs (i.e., simultaneously interact with multiple partners). Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB) protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques. Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions. We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.PLoS ONE 01/2013; 8(2):e56833. · 3.73 Impact Factor