  Beta-turn prediction is useful in protein function studies and experimental
design. Although recent approaches using machine-learning techniques such as
SVM, neural networks, and K-NN have achieved good results for beta-turn
pre-diction, there is still significant room for improvement. As previous
predictors utilized features in a sliding window of 4-20 residues to capture
interactions among sequentially neighboring residues, such feature engineering
may result in incomplete or biased features, and neglect interactions among
long-range residues. Deep neural networks provide a new opportunity to address
these issues. Here, we proposed a deep dense inception network (DeepDIN) for
beta-turn prediction, which takes advantages of the state-of-the-art deep
neural network design of the DenseNet and the inception network. A test on a
recent BT6376 benchmark shows that the DeepDIN outperformed the previous best
BetaTPred3 significantly in both the overall prediction accuracy and the
nine-type beta-turn classification. A tool, called MUFold-BetaTurn, was
developed, which is the first beta-turn prediction tool utilizing deep neural
networks. The tool can be downloaded at
http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html.
