MUFold-BetaTurn：一个用于蛋白质β-转角预测的深度密集起始网络
MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction
摘要：
β-转角预测在蛋白质功能研究和实验设计中非常有用。虽然最近的方法，使用机器学习技术，如SVM，神经网络，和K-NN取得了良好的结果，β转预测，仍然有显着的改进空间。由于先前的预测器利用4-20个残基的滑动窗口中的特征来捕获顺序相邻残基之间的相互作用，因此这种特征工程化可能导致不完整或有偏差的特征，并且忽略了长程残基之间的相互作用。深度神经网络为解决这些问题提供了新的机会。在这里，我们提出了一种用于β转弯预测的深度密集初始网络（DeepDIN），它利用了DenseNet和初始网络的最先进的深度神经网络设计。在最近的BT 6376基准测试上进行的测试表明，DeepDIN在整体预测准确率和九型β转弯分类方面都明显优于之前最好的BetaTPred 3。开发了一种名为MUFold-BetaTurn的工具，这是第一个利用深度神经网络的beta-turn预测工具。该工具可在http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html下载。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
