title: ZPRED: predicting the distance to the membrane center for residues in alpha-helical membrane proteins.
abstract: BACKGROUND
  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 alpha-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 2.55A and 68.6% of the residues are predicted within 3A of the target Z-coordinate in the 5-25A region. ZPRED is also able to predict the maximum protrusion of a loop to within 3A for 78% of the loops in the dataset.
  
  
  BACKGROUND
  Supplementary information and training data is available at http://www.sbc.su.se/~erikgr/.
authors.affiliation: Center for Biomembrane Research, Stockholm University, SE-106 91 Stockholm, Sweden.
authors.forename: Erik
authors.initials: E
authors.lastname: Granseth
authors.forename: Håkan
authors.initials: H
authors.lastname: Viklund
authors.forename: Arne
authors.initials: A
authors.lastname: Elofsson
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D002462:Cell Membrane / Q000737:chemistry*; D003198:Computer Simulation; D008390:Markov Chains; D008560:Membrane Fluidity; D008565:Membrane Proteins / Q000737:chemistry*; D008954:Models, Biological; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D008565:Membrane Proteins