CNRS, ISV, UPR2355, Bâtiment 23A, 1 avenue de la Terrasse, F-91198, Gif-sur-Yvette Cedex, France.
Rapid Commun Mass Spectrom. 2013 Feb 15;27(3):443-50. doi: 10.1002/rcm.6474.
Some large-scale proteomics studies in which strong cation exchange chromatography has been applied are used to determine proteomes and post-translational modification dynamics. Although such datasets favour the characterisation of thousands of modified peptides, e.g., phosphorylated and N-α-acetylated, a large fraction of the acquired spectra remain unexplained by standard proteomics approaches. Thus, advanced data processing allows characterisation of a significant part of these unassigned spectra.
Our recent investigation of the N-α-acetylation status of plant proteins gave a dataset of choice to investigate further the in-depth characterisation of peptide modifications using Mascot tools associated with relevant validation processes. Such an approach allows to target frequently occurring modifications such as methionine oxidation, phosphorylation or N-α-acetylation, but also the less usual peptide cationisation. Finally, this dataset offers the unique opportunity to determine the overall influence of some of these modifications on the identification score.
Although methionine oxidation has no influence and tends to favour the characterisation of protein N-terminal peptides, peptide alkalinisation shows an adverse effect on peptide average score. Nevertheless, peptide cationisation appears to favour the characterisation of protein C-terminal peptides with a limited to no direct influence on the identification score. Unexpectedly, our investigation reveals the unfortunate combination of the molecular weight of N-α-acetylation and potassium cation that mimics the mass increment of a phosphorylation group.
Since these characterisations rely upon computational treatment associated with statistical validation approaches such as 'False discovery rates' calculation or post-translational modification position validation, our investigation highlights the limitation of such treatment which is biased by the initial searched hypotheses.
一些应用强阳离子交换色谱的大规模蛋白质组学研究用于确定蛋白质组和翻译后修饰动态。尽管这些数据集有利于对数千种修饰肽进行特征描述，例如磷酸化和 N-α-乙酰化，但很大一部分获得的光谱仍然无法通过标准蛋白质组学方法解释。因此，先进的数据处理允许对这些未分配光谱的很大一部分进行特征描述。
我们最近对植物蛋白的 N-α-乙酰化状态的研究提供了一个数据集，用于进一步研究使用与相关验证过程相关的 Mascot 工具对肽修饰进行深入特征描述。这种方法可以针对经常发生的修饰，如甲硫氨酸氧化、磷酸化或 N-α-乙酰化，以及不太常见的肽阳离子化进行靶向。最后，该数据集提供了一个独特的机会来确定这些修饰中的一些对鉴定分数的总体影响。
尽管甲硫氨酸氧化没有影响，并且倾向于有利于蛋白质 N 端肽的特征描述，但肽碱化对肽平均得分有不利影响。然而，肽阳离子化似乎有利于蛋白质 C 端肽的特征描述，对鉴定分数的直接影响有限或没有。出乎意料的是，我们的研究揭示了 N-α-乙酰化和钾阳离子的分子量的不幸组合，模拟了磷酸基团的质量增量。
由于这些特征描述依赖于计算处理，以及统计验证方法，如“假发现率”计算或翻译后修饰位置验证，我们的研究强调了这种处理的局限性，这种处理受到初始搜索假设的偏见。
