The next-generation sequencing technologies, among them exome sequencing, have now brought the dream of individual genome identification close to reality. However, new advances in genome sequencing are necessary but not sufficient for identifying functionally important variants and understanding the origins of many diseases. Specific human phenotype is largely determined by stability, activity, and interactions between numerous biomolecules which work together to provide specific cellular functions. Although the majority of genetic variations are likely to be neutral, a substantial fraction of them might explain the origins of Mendelian and complex diseases. Somatic mutations may contribute significantly to tumorigenesis, and driver mutations may allow cancer cells to sustain proliferative signaling. However, finding functionally important mutations and predicting their molecular mechanisms largely remains an unsolved problem. If a disease is caused by a malfunction of a particular protein, the effects caused by missense mutations can be pinpointed by in silico modeling and it makes it more feasible to find a treatment that will reverse the effect. Signaling networks involve a dense network of protein interactions and at the same time are often deregulated in many diseases including cancer. Therefore the analysis of protein complexes, disease-related interaction networks and the effect of disease mutations on network properties would give us important clues for understanding the molecular mechanisms of diseases and allow their treatment and prevention. In fact many disease mutations are located on protein binding interfaces and may affect the specificity of recognition and protein binding affinity. There are different ways to modulate in a cell the nature and strength of protein-protein interactions and thereby regulate protein binding and coordinate functions of different pathways. Reversible phosphorylation is one of the important regulatory mechanisms;it may cause protein conformational changes and affect the binding affinity or specificity of interactions. Moreover there are many known disease missense mutations which disrupt and alter the phosphorylation patterns.

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Zhao, Feiyang; Zheng, Lei; Goncearenco, Alexander et al. (2018) Computational Approaches to Prioritize Cancer Driver Missense Mutations. Int J Mol Sci 19:
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