title: Evaluation of transmembrane helix predictions in 2014.
abstract: Experimental structure determination continues to be challenging for membrane proteins. Computational prediction methods are therefore needed and widely used to supplement experimental data. Here, we re-examined the state of the art in transmembrane helix prediction based on a nonredundant dataset with 190 high-resolution structures. Analyzing 12 widely-used and well-known methods using a stringent performance measure, we largely confirmed the expected high level of performance. On the other hand, all methods performed worse for proteins that could not have been used for development. A few results stood out: First, all methods predicted proteins in eukaryotes better than those in bacteria. Second, methods worked less well for proteins with many transmembrane helices. Third, most methods correctly discriminated between soluble and transmembrane proteins. However, several older methods often mistook signal peptides for transmembrane helices. Some newer methods have overcome this shortcoming. In our hands, PolyPhobius and MEMSAT-SVM outperformed other methods.
authors.affiliation: Department of Informatics & Center for Bioinformatics & Computational Biology-i12, Technische Universität München (TUM), Garching/Munich, 85748, Germany.
authors.forename: Jonas
authors.initials: J
authors.lastname: Reeb
authors.forename: Edda
authors.initials: E
authors.lastname: Kloppmann
authors.forename: Michael
authors.initials: M
authors.lastname: Bernhofer
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D015233:Models, Statistical; D017433:Protein Structure, Secondary
chemical_list: D008565:Membrane Proteins
keywords: evaluation; membrane protein; transmembrane helices; transmembrane helix; transmembrane helix prediction; α-helical membrane protein