LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers

Background
Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding.
Result
In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets.
Conclusion
LAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/.

【背景】赖氨酸乙酰化（Lysine acetylation）是一类关键的蛋白质翻译后修饰（protein post-translational modification）类型，参与诸多重要的细胞生命进程与重大疾病的发生发展。然而，通过传统实验方法鉴定蛋白质乙酰化位点不仅耗时耗力，且难以快速完成大规模乙酰化位点的识别工作。因此，计算方法对于加速赖氨酸乙酰化位点的挖掘仍具有重要价值。
【结果】本研究深入探究了乙酰化位点的多项生物学特征，包括乙酰化位点上下游的氨基酸序列、氨基酸的理化性质以及相邻氨基酸的转移概率。随后利用逻辑回归（logistic regression）方法整合这些信息，构建了一款新型赖氨酸乙酰化预测系统，命名为LAceP。与现有方法相比，LAceP的性能优于多数当前顶尖方法；尤为突出的是，LAceP对正负样本数据集的预测能力更为均衡。
【结论】LAceP能够整合多种生物学特征，以高精度预测赖氨酸乙酰化位点。其在线网页服务器已免费开放，访问地址为http://www.scbit.org/iPTM/