Prediction of MoRFs based on sequence properties and convolutional neural networks

BACKGROUND: Intrinsically disordered proteins possess flexible 3-D structures, which makes them play an important role in a variety of biological functions. Molecular recognition features (MoRFs) act as an important type of functional regions, which are located within longer intrinsically disordered regions and undergo disorder-to-order transitions upon binding their interaction partners. RESULTS: We develop a method, MoRF(CNN), to predict MoRFs based on sequence properties and convolutional neural networks (CNNs). The sequence properties contain structural and physicochemical properties which are used to describe the differences between MoRFs and non-MoRFs. Especially, to highlight the correlation between the target residue and adjacent residues, three windows are selected to preprocess the selected properties. After that, these calculated properties are combined into the feature matrix to predict MoRFs through the constructed CNN. Comparing with other existing methods, MoRF(CNN) obtains better performance. CONCLUSIONS: MoRF(CNN) is a new individual MoRFs prediction method which just uses protein sequence properties without evolutionary information. The simulation results show that MoRF(CNN) is effective and competitive.