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.

For publication of results, please cite:

Tsaousis G.N., Bagos P.G., Hamodrakas S.J.
HMMpTM: Improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction.
Biochim Biophys Acta, (2013) 1844(2): 316-322
doi: 10.1016/j.bbapap.2013.11.001

University of Athens
Faculty of Biology
Biophysics & Bioinformatics Laboratory