Abstract
Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein’s essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of Saccharomyces cerevisiae. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.
Highlights
Essential proteins are cellular functional molecules that are indispensable to the survival or reproduction of a living organism
To evaluate the performance of the proposed OGN centrality measure and the ensemble method, the protein interaction networks (PINs) and gene expression data of Saccharomyces cerevisiae were used, as it has been well characterized by knockout experiments and widely used in the evaluation of methods for essential protein discovery
Larger sample size is preferable because it can more effectively capture gene expression patterns; the experiments that are devoted to specific special treatments would not be suitable since they usually can only get limited number of expressed genes; the gene expression profiles are collected from same sample under multiple time points
Summary
Essential proteins are cellular functional molecules that are indispensable to the survival or reproduction of a living organism. Essential protein identification is crucial for understanding the minimal requirements of basic cell functions, and identifying human disease genes [1] and new drug targets [2]. Experimental methods for the discovery of essential proteins are often time-consuming, laborious, and costly. Computational methods can help to rank the genes based on publicly available biological resources and so greatly reduce the experimental cost needed for finding a novel gene target. With the accumulation of high-throughput experimental data, it’s possible to predict protein essentiality in network level. Many researchers have explored the correlations between network topological features and protein essentiality, and found that proteins highly connecting with other proteins in PIN are more likely to be essential than those of low connections
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