Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions

Abstract

Golgi apparatus is an important subcellular organelle that participates the secretion pathway. The role of Golgi apparatus in cellular process is related with Golgi-resident proteins. Knowing the sub-Golgi locations of Golgi-resident proteins is helpful in understanding their molecular functions. In this work, we proposed a computational method to predict the sub-Golgi locations for the Golgi-resident proteins. We take three sub-Golgi locations into consideration: the cis-Golgi network (CGN), the Golgi stack and the trans-Golgi network (TGN). By combining Pseudo-Amino Acid Compositions (Type-II PseAAC) and the Functional Domain Enrichment Score (FunDES), our method not only achieved better performances than existing methods, but also capable of recognizing proteins of the Golgi stack location, which is never considered in other state-of-the-art works.