SpecOMS: A Full Open Modification Search Method Performing All-to-All Spectra Comparisons within Minutes

The analysis of discovery proteomics experiments relies on algorithms that identify peptides from their tandem mass spectra. The almost exhaustive interpretation of these spectra remains an unresolved issue. At present, an important number of missing interpretations is probably due to peptides displaying post-translational modifications and variants that yield spectra that are particularly difficult to interpret. However, the emergence of a new generation of mass spectrometers that provide high fragment ion accuracy has paved the way for more efficient algorithms. We present a new software, SpecOMS, that can handle the computational complexity of pairwise comparisons of spectra in the context of large volumes. SpecOMS can compare a whole set of experimental spectra generated by a discovery proteomics experiment to a whole set of theoretical spectra deduced from a protein database in a few minutes on a standard workstation. SpecOMS can ingeniously exploit those capabilities to improve the peptide identification process, allowing strong competition between all possible peptides for spectrum interpretation. Remarkably, this software resolves the drawbacks (i.e., efficiency problems and decreased sensitivity) that usually accompany open modification searches. We highlight this promising approach using results obtained from the analysis of a public human data set downloaded from the PRIDE (PRoteomics IDEntification) database.

发现型蛋白质组学实验的分析环节，依赖于用于从串联质谱（tandem mass spectra）中鉴定肽段的算法。对这类质谱谱图进行近乎穷尽的解析，仍是一个尚未解决的难题。当前，大量未被成功解析的谱图或许可归因于携带翻译后修饰（post-translational modifications）与变异体的肽段——这类肽段所产生的质谱谱图尤其难以解析。然而，新一代能够实现高碎片离子精度检测的质谱仪的问世，为更高效的算法开发铺平了道路。本文介绍一款全新软件SpecOMS，其可应对大规模场景下谱图两两比对所带来的计算复杂度问题。在标准工作站上，SpecOMS可在数分钟内完成整套比对流程：将发现型蛋白质组学实验产生的实验质谱谱图集，与从蛋白质数据库推导得到的理论质谱谱图集进行匹配。SpecOMS可巧妙利用上述特性优化肽段鉴定流程，使所有潜在肽段均可围绕谱图解析任务展开充分竞争。值得注意的是，该软件解决了开放式修饰搜索（open modification searches）通常伴随的效率低下与灵敏度降低等缺陷。本研究通过分析从PRIDE（PRoteomics IDEntification）数据库下载的公开人类数据集得到的结果，对这一极具前景的方法进行了验证与展示。