title: TMKink: a method to predict transmembrane helix kinks.
abstract: A hallmark of membrane protein structure is the large number of distorted transmembrane helices. Because of the prevalence of bends, it is important to not only understand how they are generated but also to learn how to predict their occurrence. Here, we find that there are local sequence preferences in kinked helices, most notably a higher abundance of proline, which can be exploited to identify bends from local sequence information. A neural network predictor identifies over two-thirds of all bends (sensitivity 0.70) with high reliability (specificity 0.89). It is likely that more structural data will allow for better helix distortion predictors with increased coverage in the future. The kink predictor, TMKink, is available at http://tmkinkpredictor.mbi.ucla.edu/.
authors.affiliation: Medical Scientist Training Program, UCLA-DOE Institute for Genomics and Proteomics, Molecular Biology Institute, UCLA, Los Angeles, California 90095-1570, USA.
authors.forename: Alejandro D
authors.initials: AD
authors.lastname: Meruelo
authors.forename: Ilan
authors.initials: I
authors.lastname: Samish
authors.forename: James U
authors.initials: JU
authors.lastname: Bowie
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011392:Proline / Q000737:chemistry*; D017433:Protein Structure, Secondary
chemical_list: D008565:Membrane Proteins; D011392:Proline