基于质谱的蛋白质N末端乙酰化程度定量方法的研究

随着质谱技术及各种定量方法的不断完善和发展,定量蛋白质组学的方法不断地被应用到各类生物学研究中。蛋白质组学定性定量数据的处理主要通过一些多功能的商业化或者开源软件来进行,如常用的数据分析软件Proteome Discoverer和Maxquant。但是在通过化学标记对蛋白质N末端乙酰化程度进行定量这一方面,Proteome Discoverer和Maxquant在一定程度上存在准确性不高和完整度不够的问题。于是本研究针对自己的实验特点,通过Java算法编写了相应的定量程序Acequant来完成N末端乙酰化程度的相对定量。本研究将该程序在已有相关报道的He La cell上进行了验证,Acequant共定量到1 587个蛋白质N末端,而Proteome Discoverer和Maxquant分别只定量到42个和306个N末端。同时,手动验证原始图谱也证实了Acequant定量的准确性更好。于是,本研究将此方法进一步应用到秀丽隐杆线虫N末端乙酰化的研究中,并初步发现了线虫整体的N末端乙酰化状态,为进一步的N末端研究提供了支持。

Study on Quantification Method of N-terminal Acetylation of Protein Based on Mass Spectrometry

With the continuous improvement and development of mass spectrometry and various quantitative methods,quantitative proteomics strategies have been applied to different biological studies. Much commercial and open-source software have been developed for qualitative and quantitative data in proteomics studies, such as e.g., Proteome Discoverer and Maxquant that are commonly used for data analysis. However, on the quantification of N-terminal acetylation of protein by chemical labeling, the Proteome Discoverer and Maxquant have the problems of low accuracy and insufficient completeness. Herein, according to the characteristics of our experiments, we reported the corresponding quantitative program Acequant software through JAVA software for quantitative proteomics studies of protein N-terminal acetylation. The program was verified on the reported He La cell dataset, Acequant quantitated 1588 N-terminal acetylation, whereas Proteome Discoverer and Maxquant only quantitated 42 and 306 N-terminal acetylation, respectively. Manual validation of commonly quantitated dataset revealed that Acequant also demonstrated better performance in terms of quantitative accuracy. Moreover, we applied Acequant in the N-acetylomics study of C.elegans and discovered its N-acetylome profile. And it would provide support for the further N-terminal studies.