A deep dense inception network for protein beta‐turn prediction. Issue 1 (23rd July 2019)

• Record Type: Journal Article
• Authors: Fang, Chao; Shang, Yi; Xu, Dong
• Is Part Of: Proteins. Volume 88:Issue 1(2020)
• Page Start: 143
• Page End: 151
• Publication Date: 2019-07-23
• Subjects: deep learning -- deep neural network -- dense network -- inception network -- protein beta turn -- protein structure prediction; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.25780
• Languages: English
• ISSNs: 0887-3585

Abstract

Abstract: Beta‐turn prediction is useful in protein function studies and experimental design. Although recent approaches using machine‐learning techniques such as support vector machine (SVM), neural networks, and K nearest neighbor have achieved good results for beta‐turn prediction, 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 advantage of the state‐of‐the‐art deep neural network design of dense networks and inception networks. A test on a recent BT6376 benchmark data set shows that DeepDIN outperformed the previous best tool BetaTPred3 significantly in both the overall prediction accuracy and the nine‐type beta‐turn classification accuracy. 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 .