A sequence-based computational method for prediction of MoRFs

Molecular recognition features (MoRFs) are relatively short segments (10-70 residues) within intrinsically disordered regions (IDRs) that can undergo disorder-to-order transitions during binding to partner proteins. Since MoRFs play key roles in important biological processes such as signaling and regulation, identifying them is crucial for a full understanding of the functional aspects of the IDRs. However, given the relative sparseness of MoRFs in protein sequences, the accuracy of the available MoRF predictors is often inadequate for practical usage, which leaves a significant need and room for improvement. In this work, we developed a novel sequence-based predictor for MoRFs using a support vector machine (SVM) algorithm. First, we constructed a comprehensive dataset of annotated MoRFs with the wide length between 10 and 70 residues. Our method firstly utilized the flanking regions to define the negative samples. Then, amino acid composition (AAC) and two previously unexplored features including composition, transition and distribution (CTD) and K nearest neighbors (KNN) score were used to characterize sequence information of MoRFs. Finally, using five-fold cross-validation, an overall accuracy of 75.75% was achieved through feature evaluation and optimization. When performed on an independent test set of 110 proteins, the method also yielded a promising accuracy of 64.98%. Additionally, through external validation on the negative samples, our method still shows comparative performance with other existing methods. We believe that this study will be useful in elucidating the mechanism of MoRFs and facilitating hypothesis-driven experimental design and validation.