Department of Chemistry, Nanchang University, Nanchang, China.
PLoS One. 2012;7(11):e49108. doi: 10.1371/journal.pone.0049108. Epub 2012 Nov 16.
Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.
蛋白质赖氨酸乙酰化是一种可逆的翻译后修饰，在许多细胞过程中起着至关重要的作用，如转录调控、细胞凋亡和细胞因子信号转导。为了充分阐明与乙酰化相关的生物学过程的分子机制，最初但至关重要的一步是识别乙酰化底物和相应的乙酰化位点。在这项研究中，我们开发了一种基于支持向量机的位置特异性方法 PSKAcePred，用于赖氨酸乙酰化预测。根据其熵值选择或排除乙酰化位点周围的残基。我们结合了氨基酸组成信息、进化相似性和物理化学性质的特征来预测赖氨酸乙酰化位点。该预测模型在平衡正负样本的 10 折交叉验证中达到了 79.84%的准确率和 59.72%的马修斯相关系数。特征分析表明，该方法中应用的所有特征都有助于乙酰化过程。位置特异性分析表明，来自关键相邻残基的特征对乙酰化位点的确定有很大的贡献。本文的详细分析有助于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。
