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Beta-turn prediction is useful in protein function studies and experimentaldesign. Although recent approaches using machine-learning techniques such asSVM, neural networks, and K-NN have achieved good results for beta-turnpre-diction, there is still significant room for improvement. As previouspredictors utilized features in a sliding window of 4-20 residues to captureinteractions among sequentially neighboring residues, such feature engineeringmay result in incomplete or biased features, and neglect interactions amonglong-range residues. Deep neural networks provide a new opportunity to addressthese issues. Here, we proposed a deep dense inception network (DeepDIN) forbeta-turn prediction, which takes advantages of the state-of-the-art deepneural network design of the DenseNet and the inception network. A test on arecent BT6376 benchmark shows that the DeepDIN outperformed the previous bestBetaTPred3 significantly in both the overall prediction accuracy and thenine-type beta-turn classification. A tool, called MUFold-BetaTurn, wasdeveloped, which is the first beta-turn prediction tool utilizing deep neuralnetworks. The tool can be downloaded athttp://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html.

MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction