ZPRED: Predicting the distance to the membrane center for residues in α-helical membrane proteins

Abstract

Motivation: Prediction methods are of great importance for membrane proteins as experimental information is harder to obtain than for globular proteins. As more membrane protein structures are solved it is clear that topology information only provides a simplified picture of a membrane protein. Here, we describe a novel challenge for the prediction of α-helical membrane proteins: to predict the distance between a residue and the center of the membrane, a measure we define as the Z - coordinate. Even though the traditional way of depicting membrane protein topology is useful, it is advantageous to have a measure that is based on a more "physical" property such as the Z - coordinate, since it implicitly contains information about re-entrant helices, interfacial helices, the tilt of a transmembrane helix and loop lengths. Results: We show that the Z - coordinate can be predicted using either artificial neural networks, hidden Markov models or combinations of both. The best method, ZPRED, uses the output from a hidden Markov model together with a neural network. The average error of ZPRED is 2.55Å and 68.6% of the residues are predicted within 3Å of the target Z - coordinate in the 5 - 25Å region. ZPRED is also able to predict the maximum protrusion of a loop to within 3Å for 78% of the loops in the dataset. © 2006 Oxford University Press.