Department of Computer Science and Information Engineering, National Central University, Taoyuan, 32001, Taiwan.
School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, 518172, China.
Sci Rep. 2020 Jun 29;10(1):10541. doi: 10.1038/s41598-020-67384-w.
Protein malonylation, a reversible post-translational modification of lysine residues, is associated with various biological functions, such as cellular regulation and pathogenesis. In proteomics, to improve our understanding of the mechanisms of malonylation at the molecular level, the identification of malonylation sites via an efficient methodology is essential. However, experimental identification of malonylated substrates via mass spectrometry is time-consuming, labor-intensive, and expensive. Although numerous methods have been developed to predict malonylation sites in mammalian proteins, the computational resource for identifying plant malonylation sites is very limited. In this study, a hybrid model incorporating multiple convolutional neural networks (CNNs) with physicochemical properties, evolutionary information, and sequenced-based features was developed for identifying protein malonylation sites in mammals. For plant malonylation, multiple CNNs and random forests were integrated into a secondary modeling phase using a support vector machine. The independent testing has demonstrated that the mammalian and plant malonylation models can yield the area under the receiver operating characteristic curves (AUC) at 0.943 and 0.772, respectively. The proposed scheme has been implemented as a web-based tool, Kmalo (https://fdblab.csie.ncu.edu.tw/kmalo/home.html), which can help facilitate the functional investigation of protein malonylation on mammals and plants.
蛋白质丙二酰化，一种赖氨酸残基的可逆翻译后修饰，与多种生物学功能有关，如细胞调节和发病机制。在蛋白质组学中，为了更好地了解丙二酰化在分子水平上的机制，通过一种有效的方法来鉴定丙二酰化位点是必不可少的。然而，通过质谱法对丙二酰化底物进行实验鉴定既耗时又费力，且成本高昂。尽管已经开发了许多方法来预测哺乳动物蛋白质中的丙二酰化位点，但用于鉴定植物丙二酰化位点的计算资源非常有限。在这项研究中，开发了一种结合了多种卷积神经网络（CNN）与理化性质、进化信息和基于序列特征的混合模型，用于鉴定哺乳动物中的蛋白质丙二酰化位点。对于植物丙二酰化，使用支持向量机将多个 CNN 和随机森林集成到二次建模阶段。独立测试表明，哺乳动物和植物丙二酰化模型的接收者操作特征曲线（ROC）下面积（AUC）分别为 0.943 和 0.772。该方案已被实现为一个基于网络的工具，Kmalo（https://fdblab.csie.ncu.edu.tw/kmalo/home.html），它可以帮助促进哺乳动物和植物中蛋白质丙二酰化的功能研究。
