Peptide identification from tandem mass spectrometry data is a fundamental task in computational proteomics. Traditional algorithms perform well when facing unmodified peptides. However, when peptides have post-translational modifications (PTMs), these methods cannot provide satisfactory results. Recently, open search methods have been proposed to identify peptides with PTMs. While the performance of these new methods is promising, the identification results vary greatly with respect to the quality of tandem mass spectra and the number of PTMs in peptides. This motivates us to systematically study the relationship between the performance of open search methods and the quality parameters of tandem mass spectrometry data as well as the number of PTMs in peptides. In this paper, we have proposed an analytical model derived from simulated data to describe the relationship between the probability of obtaining correct results and the spectrum quality as well as the number of PTMs. The proposed model is verified using 1,464,146 real experimental spectra. The consistent trend observed in both simulated data and real data reveals the necessary conditions to effectively apply open search methods. Source code of our study is available at http://bioinformatics.ust.hk/PST.html .

中文翻译：

---

了解开放搜索在翻译后修饰肽鉴定中的局限性——基于模拟的研究

从串联质谱数据中鉴定肽是计算蛋白质组学中的一项基本任务。面对未修饰的肽段时，传统算法表现良好。然而，当肽具有翻译后修饰 (PTM) 时，这些方法无法提供令人满意的结果。最近，已经提出了开放搜索方法来识别具有 PTM 的肽。虽然这些新方法的性能是有希望的，但鉴定结果在串联质谱的质量和肽中 PTM 的数量方面差异很大。这促使我们系统地研究开放搜索方法的性能与串联质谱数据的质量参数以及肽中 PTM 数量之间的关系。在本文中，我们提出了一个基于模拟数据的分析模型来描述获得正确结果的概率与频谱质量以及 PTM 数量之间的关系。使用 1,464,146 个实际实验光谱验证了所提出的模型。在模拟数据和真实数据中观察到的一致趋势揭示了有效应用开放搜索方法的必要条件。我们研究的源代码可在http://bioinformatics.ust.hk/PST.html .