Identifying Acetylation Protein by fusing its PseAAC and Functional Domain Annotation
Identifying Acetylation Protein by fusing its PseAAC and Functional Domain Annotation
复制标题
通过融合 PseAAC 和功能域注释来识别乙酰化蛋白
DOI：
10.3389/fbioe.2019.00311
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
肖绚
中科院分区：
文献类型：
--
作者：
邱望仁;徐奥;许召春;张春花;肖绚
乙酰化是翻译后修饰（PTM）的一种，它通常与乙酸反应并将乙酰基带到有机化合物中。正确识别乙酰化蛋白有助于理解生物系统中乙酰化的机制。虽然许多乙酰化位点已经通过质谱的高通量实验研究被鉴定出来，但仍有大量的乙酰化位点需要被发现。计算方法已显示出其强大的功能，用于识别乙酰化位点的信息学技术，通常可以降低实验成本，提高效率和效率。事实上，如果有一种方法可以区分乙酰化蛋白质和非乙酰化蛋白质，这无疑是一个非常有意义和有效的方法。本文提出了一种新的基于功能域注释和亚细胞定位信息的计算方法，通过灰色系统模型和KNN得分从序列保守性信息中提取特征来识别乙酰化蛋白。作者对三个数据集进行了5重交叉验证，沿着了大量的特征分析和Relief特征选择算法。所得到的准确度都是令人满意的，作为平均性能，准确度为77.10%，马修的相关系数为0.5457，AUC值为0.8389。这些工作为相关实验验证和其他PTM工艺的进一步研究提供了有益的启示。为了方便相关研究人员，建立了名为“iACetyP”的网络服务器，可在http://www.jci-bioinfo.cn/iAcetyP上访问。
Acetylation is one of post-translational modification (PTM), which often reacts with acetic acid and brings an acetyl radical to an organic compound. It is helpful to identify acetylation protein correctly for understanding the mechanism of acetylation in biological systems. Although many acetylation sites have been identified by high throughput experimental studies via mass spectrometry, there still are lots of acetylation sites need to be discovered. Computational methods have showed their power for identifying acetylation sites with informatics techniques which usually reduce experiment cost and improve the effectiveness and efficiency. In fact, if there is an approach can distinguish the acetylated proteins from the non-acetylated ones, it is no doubt a very meaningful and effective method for this issue. Here, we proposed a novel computational method for identifying acetylation proteins by extracting features from the conservation information of sequence via gray system model and KNN scores based on the information of functional domain annotation and subcellular localization. The authors have performed the 5-fold cross-validation on three datasets along with much analysis of features and the Relief feature selection algorithm. The obtained accuracies are all satisfactory, as the mean performance, the accuracy is 77.10%, the Matthew's correlation coefficient is 0.5457, and the AUC value is 0.8389. These works might provide useful insights for the related experimental validation, and further studies of other PTM process. For the convenience of related researchers, the web-server named “iACetyP” was established and is accessible at http://www.jci-bioinfo.cn/iAcetyP.