OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar

TLDR

α‑helical transmembrane proteins comprise about 25 % of a typical genome and play essential roles, yet experimental structural determination is difficult, making improved computational topology prediction methods crucial. OCTOPUS, a novel predictor that combines hidden Markov models and artificial neural networks, achieves 94 % accuracy on 124 benchmark sequences and uniquely incorporates reentrant/membrane‑dipping regions and transmembrane hairpins into its topological grammar. OCTOPUS is available as a web server at http://octopus.cbr.su.se (contact [e-mail]); supplementary data are provided online.

Abstract

Abstract Motivation: As α-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. Availability: OCTOPUS is available as a web server at http://octopus.cbr.su.se. Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.