Identification of post-translational modifications via blind search of mass-spectra.

Dekel Tsur, Stephen Tanner, Ebrahim Zandi, Vineet Bafna, Pavel A Pevzner
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引用次数: 51

Abstract

Post-translational modifications (PTMs) are of great biological importance. Most existing approaches perform a restrictive search that can only take into account a few types of PTMs and ignore all others. We describe an unrestrictive PTM search algorithm that searches for all types of PTMs at once in a blind mode, i.e., without knowing which PTMs exist in a sample. The blind PTM identification opens a possibility to study the extent and frequencies of different types of PTMs, still an open problem in proteomics. Using our new algorithm, we were able to construct a two-dimensional PTM frequency matrix that reflects the number of MS/MS spectra in a sample for each putative PTM type and each amino acid. Application of this approach to a large IKKb dataset resulted in the largest set of PTMs reported for a single MS/MS sample so far. We demonstrate an excellent correlation between high values in the PTM frequency matrix and known PTMs thus validating our approach. We further argue that the PTM frequency matrix may reveal some still unknown modifications that warrant further experimental validation.

通过质谱盲搜索鉴定翻译后修饰。
翻译后修饰(PTMs)具有重要的生物学意义。大多数现有方法执行限制性搜索,只能考虑几种类型的ptm,而忽略所有其他类型。我们描述了一种无限制的PTM搜索算法,该算法在盲模式下搜索所有类型的PTM,即不知道样本中存在哪些PTM。PTM的盲鉴定为研究不同类型PTM的范围和频率提供了可能,这是蛋白质组学中尚未解决的问题。利用我们的新算法,我们能够构建一个二维PTM频率矩阵,该矩阵反映了样品中每种假定的PTM类型和每种氨基酸的MS/MS谱的数量。将这种方法应用于大型IKKb数据集,产生了迄今为止单个MS/MS样本报告的最大ptm集。我们证明了PTM频率矩阵中的高值与已知PTM之间的良好相关性,从而验证了我们的方法。我们进一步认为,PTM频率矩阵可能揭示了一些仍然未知的修改,需要进一步的实验验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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