Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.
Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.
J Theor Biol. 2019 Jan 14;461:92-101. doi: 10.1016/j.jtbi.2018.10.047. Epub 2018 Oct 23.
Lysine acetylation is one of the most important types of protein post-translational modifications (PTM) that are widely involved in cellular regulatory processes. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Thus, it is popular that predicts PTM sites by computational methods in recent years. Here, we developed a novel method, ProAcePred 2.0, to predict species-specific prokaryote lysine acetylation sites. In this study, we employed an efficient position-specific analysis strategy information gain method to constitute position-specific window of acetylation peptide, and then incorporated different types of features and adopted elastic net algorithm to optimize feature vectors for model learning. The prediction model achieved area under the receiver operating characteristic curve value of six species in training datasets, which are 0.78, 0.752, 0.783, 0.718, 0.839 and 0.826, of Escherichia coli, Corynebacterium glutamicum, Mycobacterium tuberculosis, Bacillus subtilis, S. typhimurium and Geobacillus kaustophilus, respectively. And our method was highly competitive for the majority of species when compared with other methods by using independent test datasets. In addition, function analyses demonstrated that different organisms were preferentially involved in different biological processes and pathways. The detailed analyses in this paper could help us to understand more of the acetylation mechanism and provide guidance for the related experimental validation. A user-friendly online web service of ProAcePred 2.0 can be freely available at http://computbiol.ncu.edu.cn/PAPred.
赖氨酸乙酰化是蛋白质翻译后修饰（PTM）的最重要类型之一，广泛参与细胞调控过程。为了全面了解乙酰化的调控机制，首先也是最重要的是鉴定乙酰化位点。然而，蛋白质乙酰化位点的实验鉴定通常既费时又昂贵。因此，近年来通过计算方法预测 PTM 位点变得非常流行。在这里，我们开发了一种新的方法 ProAcePred 2.0，用于预测种特异性原核生物赖氨酸乙酰化位点。在本研究中，我们采用了一种有效的位置特异性分析策略信息增益方法来构建乙酰化肽的位置特异性窗口，然后结合不同类型的特征，并采用弹性网络算法优化特征向量进行模型学习。该预测模型在训练数据集的六种物种中的接收者操作特征曲线下面积值分别为 0.78、0.752、0.783、0.718、0.839 和 0.826，分别为大肠杆菌、谷氨酸棒杆菌、结核分枝杆菌、枯草芽孢杆菌、鼠伤寒沙门氏菌和矿泉栖热袍菌。并且与使用独立测试数据集的其他方法相比，我们的方法在大多数物种中都具有很高的竞争力。此外，功能分析表明，不同的生物体优先参与不同的生物过程和途径。本文的详细分析可以帮助我们更好地理解乙酰化机制，并为相关的实验验证提供指导。ProAcePred 2.0 的用户友好型在线网络服务可在 http://computbiol.ncu.edu.cn/PAPred 上免费获得。
