Department of Computer Science, The University of Western Ontario, London, ON N6A 5B7, Canada.
Bioinformatics Solutions Inc. (BSI), Waterloo, ON N2L 6J2, Canada.
Bioinformatics. 2017 Dec 1;33(23):3861-3870. doi: 10.1093/bioinformatics/btx667.
Enzymatic digestion under appropriate reducing conditions followed by mass spectrometry analysis has emerged as the primary method for disulfide bond analysis. The large amount of mass spectral data collected in the mass spectrometry experiment requires effective computational approaches to automate the interpretation process. Although different approaches have been developed for such purpose, they always choose to ignore the frequently observed internal ion fragments and they lack a reasonable quality control strategy and calibrated scoring scheme for the statistical validation and ranking of the reported results.
In this research, we present a new computational approach, DISC (DISulfide bond Characterization), for matching an input MS/MS spectrum against the putative disulfide linkage structures hypothetically constructed from the protein database. More specifically, we consider different ion types including a variety of internal ions that frequently observed in mass spectra resulted from disulfide linked peptides, and introduce an effective two-layer scoring scheme to evaluate the significance of the matching between spectrum and structure, based on which we have also developed a useful target-decoy strategy for providing quality control and reporting false discovery rate in the final results. Systematic experiments conducted on both low-complexity and high-complexity datasets demonstrated the efficiency of our proposed method for the identification of disulfide bonds from MS/MS spectra, and showed its potential in characterizing disulfide bonds at the proteome scale instead of just a single protein.
Software is available for downloading at http://www.csd.uwo.ca/yliu766/.
Supplementary data are available at Bioinformatics online.
在适当的还原条件下进行酶解，然后进行质谱分析，已成为分析二硫键的主要方法。在质谱实验中收集的大量质谱数据需要有效的计算方法来自动解释过程。尽管已经开发了不同的方法来实现这一目的，但它们总是选择忽略经常观察到的内部离子片段，并且缺乏合理的质量控制策略和校准评分方案，用于对报告结果进行统计验证和排名。
在这项研究中，我们提出了一种新的计算方法 DISC（二硫键表征），用于将输入的 MS/MS 光谱与从蛋白质数据库中假设构建的可能的二硫键连接结构进行匹配。更具体地说，我们考虑了不同的离子类型，包括在源自二硫键连接肽的质谱中经常观察到的各种内部离子，并引入了一种有效的两层评分方案，以评估光谱与结构之间匹配的显著性，在此基础上，我们还开发了一种有用的目标-诱饵策略，用于在最终结果中提供质量控制和报告假发现率。在低复杂度和高复杂度数据集上进行的系统实验证明了我们提出的方法从 MS/MS 光谱中识别二硫键的效率，并表明了它在蛋白质组规模上而不仅仅是单个蛋白质上表征二硫键的潜力。
软件可在 http://www.csd.uwo.ca/yliu766/下载。
补充数据可在生物信息学在线获得。
