用多样性增量特征选择技术识别蛋白质磷酸化位点

Identification of Protein Phosphorylation Sites by Diversity Increment Feature Selection Technique

摘要: 磷酸化是最重要的蛋白质翻译后修饰之一，在许多细胞过程中扮演重要角色。发展磷酸化位点精确识别的计算生物学方法，有助于对磷酸化信号转导机制的理解。本文给出一种激酶无关的磷酸化位点识别模型，称为FSID_PhSite。模型以k间隔氨基酸对组分和位置保守氨基酸组分为特征，应用多样性增量特征选择技术进行特征筛选，将选出的特征输入到支持向量机算法进行识别。在正负样本数之比为1:1的情形下，对磷酸化丝氨酸、苏氨酸和酪氨酸在独立测试集检验，识别精度分别达到84.34%、82.32%和68.89%。结果优于现有的激酶无关磷酸化位点识别模型。

Abstract: Phosphorylation is one of the most important protein post-translational modifications and plays important roles in numerous biological processes by significantly affecting proteins’ structure and dynamics. The development of computational biological methods for the accurate identification of phosphorylation sites helps to our understanding of key signal transduction mechanisms. In this paper, a kinase independent phosphorylation site identification model was presented, called FSID_PhSite. The model is featured by component of k-spaced amino acid pairs and the position conservation of residues surrounding the phosphorylation sites. Applying diversity incremental feature selection technique to feature selection and inputting the selected features into the support vector machine algorithm for recognition, when the ratio of positive and negative samples is 1:1, on independent testing dataset validation, the accuracy of identification for serine, threonine and tyrosine sites is 84.34%, 82.32% and 68.89%, respectively. The results were superior to the existing kinase independent phosphorylation sites identification model.