Abstract
BackgroundStudies of intrinsically disordered proteins that lack a stable tertiary structure but still have important biological functions critically rely on computational methods that predict this property based on sequence information. Although a number of fairly successful models for prediction of protein disorder have been developed over the last decade, the quality of their predictions is limited by available cases of confirmed disorders.ResultsTo more reliably estimate protein disorder from protein sequences, an iterative algorithm is proposed that integrates predictions of multiple disorder models without relying on any protein sequences with confirmed disorder annotation. The iterative method alternately provides the maximum a posterior (MAP) estimation of disorder prediction and the maximum-likelihood (ML) estimation of quality of multiple disorder predictors. Experiments on data used at CASP7, CASP8, and CASP9 have shown the effectiveness of the proposed algorithm.ConclusionsThe proposed algorithm can potentially be used to predict protein disorder and provide helpful suggestions on choosing suitable disorder predictors for unknown protein sequences.
Highlights
Studies of intrinsically disordered proteins that lack a stable tertiary structure but still have important biological functions critically rely on computational methods that predict this property based on sequence information
The performance of predictors was evaluated by three criteria: the average of sensitivity and specificity (ACC), a weighted score (Sw) that considers the rates of ordered and disordered residues in the datasets, and the area under the ROC curve (AUC)
Since the disordered residues are rare in the targets, the weighted score Sw was introduced at CASP6 [25]: Sw
Summary
Studies of intrinsically disordered proteins that lack a stable tertiary structure but still have important biological functions critically rely on computational methods that predict this property based on sequence information. The prediction results of previous meta predictors may not be so good for proteins that have sequence patterns very different from cases used for integration. It achieved higher prediction accuracy than all predictors participating in CASP7 as stated in its paper [16], metaPrDOS failed to be one of the top predictors in CASP8 [22]. One of metaPrDOS’ component predictors, i.e. DISOPRED [2], was more accurate than metaPrDOS in CASP8 [22]
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