title: A comprehensive overview of computational protein disorder prediction methods.
abstract: Over the past decade there has been a growing acknowledgement that a large proportion of proteins within most proteomes contain disordered regions. Disordered regions are segments of the protein chain which do not adopt a stable structure. Recognition of disordered regions in a protein is of great importance for protein structure prediction, protein structure determination and function annotation as these regions have a close relationship with protein expression and functionality. As a result, a great many protein disorder prediction methods have been developed so far. Here, we present an overview of current protein disorder prediction methods including an analysis of their advantages and shortcomings. In order to help users to select alternative tools under different circumstances, we also evaluate 23 disorder predictors on the benchmark data of the most recent round of the Critical Assessment of protein Structure Prediction (CASP) and assess their accuracy using several complementary measures.
authors.affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Xin
authors.initials: X
authors.lastname: Deng
authors.forename: Jesse
authors.initials: J
authors.lastname: Eickholt
authors.forename: Jianlin
authors.initials: J
authors.lastname: Cheng
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012372:ROC Curve
chemical_list: D011506:Proteins