Characterizing Proteins with Finer Functions: A Case Study for Translational Functions of Yeast Proteins

Based on high-throughput data, numerous algorithms have been designed for finding functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors including the low a-priori probability of novel proteins participating in a detailed function and the lack of detailed functional knowledge for training algorithms. For such partially characterized proteins, we suggest an approach to find their finer functions based on protein-protein interaction sub-networks, which can efficiently find proteins\´ novel functions. As an application, we find that finer functions can be predicted for 18 and 15 proteins currently annotated in "protein biosynthesis" and "translation" with more than 90% precision, respectively. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.