Prediction of Essential Proteins Based on Local Interaction Density

Prediction of essential proteins which is aided by computer science and supported from high throughput data is a more efficient method compared with time consuming and expensive experimental approaches. There are many computational approaches reported, however they are usually sensitive to various network structures so that their robustness are generally poor. In this paper, a novel topological centrality measure for predicting essential proteins based on local interaction density, named as LID, is proposed. It is different from previous measures that LID takes the essentiality of a node from interaction densities among its neighbors through topological analyses of real proteins in a protein complex set first time at the viewpoint of biological modules. LID is applied to four different yeast protein interaction networks, which are obtained, respectively, from the DIP database and the BioGRID database. The experimental results show that the number of essential proteins detected by LID universally exceeds or approximates the best performance of other 10 topological centrality measures in all 24 comparisons of four networks: DC, BC, ClusterC, CloseC, MNC, SoECC(NC), LAC, SC, EigC, and InfoC. The better robustness of LID for multiple data sets will make it to be a new core topological centrality measure to improve the performance of prediction for more species protein interaction networks.