Motivation:
During the last decades a large number of computational methods have been developed for predicting transmembrane protein structure and topology. Current predictors rely on two topogenic signals in the protein sequence: the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. Here we report a Hidden Markov Model based method capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites across the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites when compared to currently available predictors.
