title: Tuning the precision of predictors to reduce overestimation of protein disorder over large datasets.
abstract: This is a study on the precision of four known protein disorder predictors, ranked among the best-performing ones: DISOPRED2, PONDR VSL2B, IUPred and ESpritz. We address here the problem of a systematic overestimation of the number of disordered proteins recognized through the use of these predictors, considered as a standard. Some of these predictors, used with their default setting, have a low precision, implying a tendency to overestimate the occurrence of disordered proteins in genome-wide surveys. Moreover, different predictors often disagree on the evaluation of individual proteins. To cope with this problem and in order to propose a simple procedure that enhances precision based on precision-recall curves, we re-tuned the discriminative thresholds of the predictors by training and cross-validating their performance on a cured dataset. After re-tuning, both the disagreement among predictors and the tendency to overestimate the occurrence of disordered proteins are reduced. This is shown in a dedicated study over the human proteome and a set of cancer-related human proteins, with no a priori disorder annotation. Simple quantitative estimates suggest that the occurrence of disorder among cancer-related proteins and other similar large-scale surveys has been overestimated in the past.
authors.affiliation: Physics Department, Sapienza University of Rome, Rome, Italy. Antonio.Deiana@Roma1.infn.it
authors.forename: Antonio
authors.initials: A
authors.lastname: Deiana
authors.forename: Andrea
authors.initials: A
authors.lastname: Giansanti
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein / Q000706:statistics & numerical data*; D015894:Genome, Human; D006801:Humans; D009363:Neoplasm Proteins / Q000737:chemistry / Q000235:genetics; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D040901:Proteomics / Q000706:statistics & numerical data*; D012984:Software
chemical_list: D009363:Neoplasm Proteins; D011506:Proteins