Novel Angiogenic Functional Targets Predicted through “Dark Matter” Assessment in Protein Networks

Abstract

In order to model protein networks we must extend our knowledge of the protein associations occurring in molecular systems and their functional relationships. We have significantly increased the accuracy of protein association predictions by the meta-statistical integration of three computational methods specifically designed for eukaryotic proteomes. From this former work it was discovered that high-throughput experimental assays seem to perform biased screenings of the real protein networks and leave important areas poorly characterized. This finding supports the convenience to combine computational prediction approaches to model protein interaction networks. We address in this work the challenge of integrating context information, present in predicted and known protein network models, to functionally characterize novel proteins. We applied a random walk-with-restart kernel to our models aiming at fixing some poorly described or unknown proteins involve in angiogenesis. This approach reveals some novel key angiogenic components within the human interactome.