Prediction of MoRFs based on sequence properties and convolutional neural networks

BioData Mining

Yue Chi¹  Yatong Zhou¹  Hao He¹  Jingfei He¹ 

[1] School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China;

关键词: Molecular recognition features;    Intrinsically disordered proteins;    Prediction;    Convolutional neural network;   

DOI  :  10.1186/s13040-021-00275-6

来源: Springer

【 摘 要 】

BackgroundIntrinsically 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.ResultsWe develop a method, MoRFCNN, 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, MoRFCNN obtains better performance.ConclusionsMoRFCNN is a new individual MoRFs prediction method which just uses protein sequence properties without evolutionary information. The simulation results show that MoRFCNN is effective and competitive.

【 授权许可】

CC BY