title: Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy.
abstract: Protein structure alignment methods are used for the detection of evolutionary and functionally related positions in proteins. A wide array of different methods are available, but the choice of the best method is often not apparent to the user. Several studies have assessed the alignment accuracy and consistency of structure alignment methods, but none of these explicitly considered membrane proteins, which are important targets for drug development and have distinct structural features. Here, we compared 13 widely used pairwise structural alignment methods on a test set of homologous membrane protein structures (called HOMEP3). Each pair of structures was aligned and the corresponding sequence alignment was used to construct homology models. The model accuracy compared to the known structures was assessed using scoring functions not incorporated in the tested structural alignment methods. The analysis shows that fragment-based approaches such as FR-TM-align are the most useful for aligning structures of membrane proteins. Moreover, fragment-based approaches are more suitable for comparison of protein structures that have undergone large conformational changes. Nevertheless, no method was clearly superior to all other methods. Additionally, all methods lack a measure to rate the reliability of a position within a structure alignment. To solve both of these problems, we propose a consensus-type approach, combining alignments from four different methods, namely FR-TM-align, DaliLite, MATT, and FATCAT. Agreement between the methods is used to assign confidence values to each position of the alignment. Overall, we conclude that there remains scope for the improvement of structural alignment methods for membrane proteins.
authors.affiliation: Computational Structural Biology Group, Max Planck Institute of Biophysics, Frankfurt Am Main, Germany.
authors.forename: Marcus
authors.initials: M
authors.lastname: Stamm
authors.affiliation: Computational Structural Biology Group, Max Planck Institute of Biophysics, Frankfurt Am Main, Germany.|Computational Structural Biology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland.
authors.forename: Lucy R
authors.identifier: http://orcid.org/0000-0003-1855-7985
authors.initials: LR
authors.lastname: Forrest
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry* / Q000235:genetics; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods; D017386:Sequence Homology, Amino Acid
chemical_list: D008565:Membrane Proteins
keywords: beta barrel; conformational change; flexible alignment; homology modeling; integral membrane protein; protein structure; structure comparison