Top-down mass spectrometry is capable of identifying whole proteoform sequences with multiple post-translational modifications because it generates tandem mass spectra directly from intact proteoforms. Many software tools, such as ProSightPC, MSPathFinder, and TopMG, have been proposed for identifying proteoforms with modifications. In these tools, various methods are employed to estimate the statistical significance of identifications. However, most existing methods are designed for proteoform identifications without modifications, and the challenge remains for accurately estimating the statistical significance of proteoform identifications with modifications. Here we propose TopMCMC, a method that combines a Markov chain random walk algorithm and a greedy algorithm for assigning statistical significance to matches between spectra and protein sequences with variable modifications. Experimental results showed that TopMCMC achieved high accuracy in estimating E-values and false discovery rates of identifications in top-down mass spectrometry. Coupled with TopMG, TopMCMC identified more spectra than the generating function method from an MCF-7 top-down mass spectrometry data set.

中文翻译：

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马尔可夫链蒙特卡洛方法，用于通过自上而下的质谱估计蛋白质形式鉴定的统计学意义。

自上而下的质谱能够识别具有多个翻译后修饰的完整蛋白形式序列，因为它直接从完整的蛋白形式产生串联质谱。已经提出了许多软件工具，例如ProSightPC，MSPathFinder和TopMG，用于识别经过修改的蛋白形式。在这些工具中，采用了各种方法来估计标识的统计意义。然而，大多数现有方法被设计用于无需修饰的蛋白形式鉴定，并且挑战在于准确估计具有修饰的蛋白形式鉴定的统计显着性。在这里，我们提出TopMCMC，一种结合了马尔可夫链随机游走算法和贪婪算法的方法，用于分配统计显着性以对具有可变修饰的光谱和蛋白质序列进行匹配。实验结果表明，TopMCMC在估计E值和自上而下质谱鉴定中的错误发现率方面具有很高的准确性。与TopMG结合使用，与MCF-7自上而下质谱数据集的生成函数方法相比，TopMCMC可以识别更多光谱。