德国马克斯·普朗克生物化学研究所的Jrgen Cox研究组与美国Alphabet旗下生命科学部门Verily的Peter Cimermancic研究组合作开发了一种高质量二级质谱预测工具。该研究成果发表在2019年6月出版的《Nature Methods》上。

肽段碎片谱的预测常规用于基于质谱的蛋白质组学分析。然而，对于科学家而言，这些片段离子的生成还没有被完全解析，因此无法准确地评估片段离子的强度。

研究人员发现机器学习能够在测量不确定时准确预测质谱中肽段碎片化的模式。此外，这个模型表明，肽段碎片化依赖于片段序列内的远程相互作用。研究人员进一步通过分析数据依赖与数据不依赖获取的数据集来验证了其模型的实用性。在前一个例子中，研究人员在肽段鉴定总数中的q值依赖型增加。在后一个例子中，研究人员确认使用预测的串联质谱光谱几乎等同于实验库中的光谱。

附：英文原文

Title: High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis

Author: Shivani Tiwary, Roie Levy, Petra Gutenbrunner, Favio Salinas Soto, Krishnan K. Palaniappan, Laura Deming, Marc Berndl, Arthur Brant, Peter Cimermancic, Jrgen Cox

Issue&Volume: Volume 16 Issue 6, June 2019

Abstract: Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.

DOI: 10.1038/s41592-019-0427-6

Source:https://www.nature.com/articles/s41592-019-0427-6