title: Identification of correct regions in protein models using structural, alignment, and consensus information.
abstract: In this study we present two methods to predict the local quality of a protein model: ProQres and ProQprof. ProQres is based on structural features that can be calculated from a model, while ProQprof uses alignment information and can only be used if the model is created from an alignment. In addition, we also propose a simple approach based on local consensus, Pcons-local. We show that all these methods perform better than state-of-the-art methodologies and that, when applicable, the consensus approach is by far the best approach to predict local structure quality. It was also found that ProQprof performed better than other methods for models based on distant relationships, while ProQres performed best for models based on closer relationship, i.e., a model has to be reasonably good to make a structural evaluation useful. Finally, we show that a combination of ProQprof and ProQres (ProQlocal) performed better than any other nonconsensus method for both high- and low-quality models. Additional information and Web servers are available at: http://www.sbc.su.se/~bjorn/ProQ/.
authors.affiliation: Stockholm Bioinformatics Center, Stockholm University, SE-106 91 Stockholm, Sweden. bjorn@sbc.su.se
authors.forename: Björn
authors.initials: B
authors.lastname: Wallner
authors.forename: Arne
authors.initials: A
authors.lastname: Elofsson
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D019295:Computational Biology; D016384:Consensus Sequence; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment; D040681:Structural Homology, Protein
chemical_list: D011506:Proteins