Events

Speaker : Ms. Dana, Research Scholar

Title : Predictive modeling of moonlighting DNA-binding proteins

Abstract: Moonlighting proteins are multifunctional, single polypeptide chains capable of performing multiple autonomous- often unrelated – functions. The majority of moonlighting proteins have been discovered through woark that did not particularly target their multifunctionality. Moonlighting has been treated as a general functional property, not grouped in terms of the primary function of such proteins. We believe that the prediction of moonlighting proteins from the first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one primary function in a single organism at a time will significantly advance our understanding of this important functional pattern of multifunctional proteins. In this work, we investigated human moonlighting DNA-binding proteins (mDBPs) in terms of properties that distinguish them from other (non-moonlighting) proteins with the same DNA-binding protein (DBP) function. Following a careful analysis of discriminatory features, a machine learning model was developed to assess the predictability of mDBPs from other DBPs (oDBPs). We observed that mDBPs can be discriminated from oDBPs with high accuracy of 74% AUC of ROC using these first principle features. A number of novel predicted mDBPs were found to have literature support for their being moonlighting and others are proposed as candidates, for which the moonlighting function is currently unknown. We believe that this work will help in deciphering and annotating novel moonlighting DBPs and scale up other primary functions.