OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar.
BACKGROUND
As alpha-helical transmembrane proteins constitute roughly 25% of a typical genome and are vital parts of many essential biological processes, structural knowledge of these proteins is necessary for increasing our understanding of such processes. Because structural knowledge of transmembrane proteins is difficult to attain experimentally, improved methods for prediction of structural features of these proteins are important.


RESULTS
OCTOPUS, a new method for predicting transmembrane protein topology is presented and benchmarked using a dataset of 124 sequences with known structures. Using a novel combination of hidden Markov models and artificial neural networks, OCTOPUS predicts the correct topology for 94% of the sequences. In particular, OCTOPUS is the first topology predictor to fully integrate modeling of reentrant/membrane-dipping regions and transmembrane hairpins in the topological grammar.


BACKGROUND
OCTOPUS is available as a web server at http://octopus.cbr.su.se.
Viklund
Håkan
H
Department of Biochemistry and Biophysics/Center for Biomembrane Research/Stockholm Bioinformatics Center, The Arrhenius Laboratories for Natural Sciences, Stockholm University, SE-10691 Stockholm, Sweden.
Elofsson
Arne
A
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D008565:Membrane Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011487:Protein Conformation; D015203:Reproducibility of Results; D012660:Semantics; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*
D008565:Membrane Proteins