蛋白质的翻译后修饰 (PTM) 可调节蛋白质的结构和功能。单个蛋白质分子可以拥有多个修饰位点，这些修饰位点可以容纳各种 PTM 类型，从而在该蛋白质上产生各种不同的模式或 PTM 组合。不同的 PTM 模式可以产生不同的生物学功能。为了促进对同一蛋白质分子的多个 PTM 的研究，自上而下的质谱 (MS) 已被证明是测量完整蛋白质质量的有用工具，从而甚至可以将远距离位点的 PTM 分配给同一蛋白质我们开发了一个名为 MSModDetector 的 Python 模块，用于研究来自单个离子质谱 (I2MS) 数据的 PTM 模式。 I2MS 是一种完整的蛋白质质谱方法，无需推断电荷态即可生成真实的质谱。该算法首先检测并量化感兴趣蛋白质的质量转移，然后使用线性编程推断潜在的 PTM 模式。该算法根据肿瘤抑制蛋白 p53 的模拟 I2MS 数据和实验 I2MS 数据进行评估。我们证明 MSModDetector 是比较不同条件下蛋白质 PTM 模式景观的有用工具。对 PTM 模式的改进分析将有助于更深入地了解 PTM 调节的细胞过程。源代码可在 https://github.com/marjanfaizi/MSModDetector 上获取。补充数据可在 Bioinformatics online 上获取。© 作者2024 年。牛津大学出版社出版。

Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs on the same protein molecule, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even PTMs at distant sites to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein.We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I2MS) data. I2MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I2MS data and experimental I2MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes.The source code is available at https://github.com/marjanfaizi/MSModDetector.Supplementary data are available at Bioinformatics online.© The Author(s) 2024. Published by Oxford University Press.