title: Comprehensive comparative assessment of in-silico predictors of disordered regions.
abstract: Intrinsic disorder is relatively common in proteins, plays important roles in numerous cellular activities, and its prevalence was implicated in various human diseases. However, annotations of the disorder lag behind the rapidly increasing number of known protein chains. The last decade observed development of a relatively large number of in-silico methods that predict the disorder using the protein sequence as their input. We perform a first-of-its kind comprehensive empirical evaluation of the disorder predictors which is characterized by three novel aspects, (1) we evaluate the quality of the disorder predictions at the residue, segment, and chain levels; (2) we consider a large number of published and accessible to the end user predictors that are evaluated on a relatively big dataset with close to 500 proteins; and (3) we assess statistical significance of differences between the considered methods. Our study reveals that there is no universally superior predictor and that the top-performing methods are complementary. We show that while recent consensus-based predictors outperform other considered methods for the residue-level predictions, some older methods perform better for the prediction of the disordered segments. Our analysis indicates that certain predictors are biased to under-predict the disorder, while some other solutions tend to over-predict the number of the disordered residues. We also evaluate the utility of the predicted residue-level disorder for prediction of proteins with long disordered segments and prediction of the chainlevel disorder content. Lastly, we provide recommendations concerning development of a new generation of consensusbased methods and specialized methods for improved prediction of the disorder content.
authors: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
authors: Zhen-Ling
authors: ZL
authors: Peng
authors: Lukasz
authors: L
authors: Kurgan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012372:ROC Curve
chemical_list: D011506:Proteins