title: Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information.
abstract: Methods that predict the topology of helical membrane proteins are standard tools when analyzing any proteome. Therefore, it is important to improve the performance of such methods. Here we introduce a novel method, PRODIV-TMHMM, which is a profile-based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods. In our tests, PRODIV-TMHMM outperforms earlier methods both when evaluated on "low-resolution" topology data and on high-resolution 3D structures. The results presented here indicate that the topology could be correctly predicted for approximately two-thirds of all membrane proteins using PRODIV-TMHMM. The importance of evolutionary information for topology prediction is emphasized by the fact that compared with using single sequences, the performance of PRODIV-TMHMM (as well as two other methods) is increased by approximately 10 percentage units by the use of homologous sequences. On a more general level, we also show that HMM-based (or similar) methods perform superiorly to methods that focus mainly on identification of the membrane regions.
authors.affiliation: Stockholm Bioinformatics Center, Stockholm University, SE-10691 Stockholm, Sweden.
authors.forename: Håkan
authors.initials: H
authors.lastname: Viklund
authors.forename: Arne
authors.initials: A
authors.lastname: Elofsson
mesh_terms: D000818:Animals; D006801:Humans; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry*; D011237:Predictive Value of Tests; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D016415:Sequence Alignment / Q000379:methods*
chemical_list: D008565:Membrane Proteins