title: TMSEG: Novel prediction of transmembrane helices.
abstract: Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bridged the gap by predicting transmembrane helices (TMHs), but room for improvement remains. Here, we present TMSEG, a novel method identifying TMPs and accurately predicting their TMHs and their topology. The method combines machine learning with empirical filters. Testing it on a non-redundant dataset of 41 TMPs and 285 soluble proteins, and applying strict performance measures, TMSEG outperformed the state-of-the-art in our hands. TMSEG correctly distinguished helical TMPs from other proteins with a sensitivity of 98 ± 2% and a false positive rate as low as 3 ± 1%. Individual TMHs were predicted with a precision of 87 ± 3% and recall of 84 ± 3%. Furthermore, in 63 ± 6% of helical TMPs the placement of all TMHs and their inside/outside topology was correctly predicted. There are two main features that distinguish TMSEG from other methods. First, the errors in finding all helical TMPs in an organism are significantly reduced. For example, in human this leads to 200 and 1600 fewer misclassifications compared to the second and third best method available, and 4400 fewer mistakes than by a simple hydrophobicity-based method. Second, TMSEG provides an add-on improvement for any existing method to benefit from. Proteins 2016; 84:1706-1716. © 2016 Wiley Periodicals, Inc.
authors: Department of Informatics & Center for Bioinformatics & Computational Biology - i12, Technische Universität München (TUM), Boltzmannstr. 3, Garching/Munich, 85748, Germany. Michael.Bernhofer@mytum.de.
authors: Michael
authors: M
authors: Bernhofer
authors: Department of Informatics & Center for Bioinformatics & Computational Biology - i12, Technische Universität München (TUM), Boltzmannstr. 3, Garching/Munich, 85748, Germany.|New York Consortium on Membrane Protein Structure, New York Structural Biology Center, New York, New York, 10027.
authors: Edda
authors: E
authors: Kloppmann
authors: Department of Informatics & Center for Bioinformatics & Computational Biology - i12, Technische Universität München (TUM), Boltzmannstr. 3, Garching/Munich, 85748, Germany.
authors: Jonas
authors: J
authors: Reeb
authors: Department of Informatics & Center for Bioinformatics & Computational Biology - i12, Technische Universität München (TUM), Boltzmannstr. 3, Garching/Munich, 85748, Germany.|New York Consortium on Membrane Protein Structure, New York Structural Biology Center, New York, New York, 10027.|Institute of Advanced Study (TUM-IAS), Lichtenbergstr. 2a, Garching/Munich, 85748, Germany.|Institute for Food and Plant Sciences WZW - Weihenstephan, Alte Akademie 8, Freising, Germany.
authors: Burkhard
authors: 0000-0003-0179-8424
authors: B
authors: Rost
mesh_terms: D030562:Databases, Protein; D066264:Datasets as Topic; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D000069550:Machine Learning; D008565:Membrane Proteins / Q000737:chemistry*; D000072756:Protein Conformation, alpha-Helical; D012680:Sensitivity and Specificity
chemical_list: D008565:Membrane Proteins
keywords:  membrane protein; protein structure prediction; transmembrane helices; transmembrane helix prediction; transmembrane protein prediction; α-helical integral membrane protein