Keywords

cancer, applications

Data Set Description Summary

The 14-3-3 family of phospho-binding proteins regulate a variety of major cellular processes through interaction with a network of dynamic proteins. Deregulation of the 14-3-3 interaction network contributes to a variety of degenerative disorders and cancers. Our lab focuses on identifying novel 14-3-3 interactions and understanding how 14-3-3 binding regulates protein function. A major gap in this process is that identifying the phospho-site where 14-3-3 docks on a given protein is time- and resource-consuming. Prediction algorithms have been developed to predict canonical 14-3-3 binding sites, however, there are many non-canonical sites that existing software is unable to predict. To fill this gap, we have used AI algorithms to identify protein characteristics that predict 14-3-3 docking phospho-sites. Based on these data, we developed an app that significantly improves 14-3-3 site predictions. As proof of principle, we have used the method to identify 14-3-3 binding sites on TNK1, a non-receptor tyrosine kinase that mediates cell survival in cancer, and AKAP13, a scaffold protein involved in regulatory activity.