Prediction of Protein Essentiality Based on Genomic Data

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Abstract
A major goal of pharmaceutical bioinformatics is to develop computational tools for systematic in silico molecular target identification. Here we demonstrate that in the yeast Saccharomyces cerevisiae the phenotypic effect of single gene deletions simultaneously correlates with fluctuations in mRNA expression profiles, the functional categorization of the gene products, and their connectivity in the yeasts protein-protein interaction network. Building on these quantitative correlations, we developed a computational method for predicting the phenotypic effect of a given genes functional disabling or removal. Our subsequent analyses were in good agreement with the results of systematic gene deletion experiments, allowing us to predict the deletion phenotype of a number of untested yeast genes. The results underscore the utility of large genomic databases for in silico systematic drug target identification in the postgenomic era.