Sequence-based prediction and characterization of disorder-to-order transitioning binding sites in proteins

Abstract

Molecular Recognition Feature (MoRF) regions are disordered binding sites that become structured upon binding. MoRFs are implicated in important biological processes, including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains. We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (, , coil, and complex). We develop a comprehensive dataset of annotated MoRFs and use it to build and empirically compare our method. Empirical evaluation shows that MoRFpred statistically significantly outperforms existing predictors by 0.07 in AUC and 10% in success rate. We show that our predicted MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We present case studies to analyze these putative MoRFs.