The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. The major need now is to explore this disparate data to find biologically relevant interactions and pathways. Thus, in the post-genomic era, there is clearly a need for the development of algorithms that can accurately predict novel protein-protein interaction networks in silico. The evolutionarily conserved Aurora family kinases have been chosen as a model for the development of a method to identify novel biological networks by a comparison of human and various model organisms. Our search methodology was designed to predict and prioritize molecular targets for Aurora family kinases, so that only the most promising are subjected to empirical testing. Four potential Aurora substrates and/or interacting proteins, TACC3, survivin, Hec1, and hsNuf2, were identified and empirically validated. Together, these results justify the timely implementation of in silico biology in routine wet-lab studies and have also allowed the application of a new approach to the elucidation of protein function in the postgenomic era. Molecular & Cellular Proteomics 3:93-104,
2004.One possible path toward understanding the biological function of a target gene is through the discovery of how it interfaces with known protein-protein interaction networks. We are only now beginning to appreciate the nature and complexity of these networks, and construction of such a network using the traditional biochemical approaches still remains a significant challenge. Recently, the application of high-throughput technologies, such as large-scale yeast twohybrid analysis, has generated an enormous amount of data (1-4). This has led researchers to often face the dilemma of how to effectively utilize the vast information gathered through these large-scale studies. Investigators relying solely on a traditional wet-lab approach for making decisions or setting research priorities are likely to find themselves outpaced by peers who combine in silico biology with empirical methods.