- Measured more than a million collision cross sections (CCS) from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) on several timsTOF Pro systems
- 来自360 LC-TIMS-MS/MS运行的大规模CCS数据，以最大处理
- With CCS alignment, across 347,885 peptide CCS values measured in duplicate, the median coefficient of variation (CV) was 0.4%; highlights excellent reproducibility of TIMS CCS over longer periods of time and across instruments
- CCS数据的精度（CV <1％）足以训练深层复发性神经网络，该网络仅基于蛋白质组肽序列准确预测CCS值（r> 0.99）
- Harnessing deep learning, CCS values can now be predicted for any peptide and organism, forming a basis for advanced 4D proteomics TIMS/PASEF workflows that make full use of the additional peptide CCS information
慕尼黑 - （商业资讯） -布鲁克公司（（Nasdaq: BRKR) today announces a seminal publication from the groups of Professors Matthias Mann and Fabian Theis in the journal自然通讯标题“深入学习肽宇宙的碰撞横截面，从一百万个实验值’作者：Florian Meier等人。（（doi.org/10.1038/S41467-021-21352-8）1。
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Fig. 1: Large-scale peptide collisional cross section (CCS) measurement with TIMS and PASEF. From "Deep learning the collisional cross sections of the peptide universe from a million experimental values". (a) Workflow from extraction of whole-cell proteomes through digestion, fractionation, and chromatographic separation of each fraction. The TIMS-quadrupole TOF mass spectrometer was operated in PASEF mode. (b) Overview of the CCS dataset in this study by organism. (c) Frequency of peptide C-terminal amino acids. (d) Frequency of peptide N-terminal amino acids. (e) Distribution of 559,979 unique data points, including modified sequence and charge state, in the CCS vs. m/z space color-coded by charge state. Density distributions for m/z and CCS are projected on the top and right axes, respectively. Source data are provided as a Source Data file. (Graphic: Business Wire)
这自然通讯论文描述了在Timstof Pro上测量的CCS值是肽离子的本质上固有特性，可用于提高4D shot弹枪蛋白质组学中对肽和蛋白质组识别的信心。由于基于质谱的蛋白质组学依赖于获得的光谱与蛋白质序列数据库的准确匹配，因此准确的CCS值可提供缩小候选者列表的好处。这对于高灵敏度蛋白质组学至关重要，其中需要在复杂的混合物中准确测量低水平的肽信号，例如在血浆蛋白质组学，肽组学，免疫肽组学或元蛋白质组学中。
该出版物总结了由马蒂亚斯·曼恩（Matthias Mann）教授领导的合作研究工作，他在德国马丁斯·纳斯林斯里德（Martinsrried）的马克斯·普朗克（Max Planck）生物化学研究所和丹麦大学哥本哈根大学诺德斯基基金会蛋白质研究中心（Novo Novo Novo Novo Novo Novo nove dimections）与丹麦哥本哈根大学（Novo Nordisk Foundation Center）一起。Fabian Theis教授还在德国环境卫生研究中心和德国Tu慕尼黑的数学系慕尼黑市慕尼黑慕尼黑双重任命。
首席作家Florian Meier博士现在是德国耶拿大学医院功能蛋白质组学助理教授，他说：“来自Timstof Pro的数据中肽CCS值的规模和精度足以训练我们的深度学习模型，以准确地训练仅基于肽序列预测CCS值。肽序列中包含的氨基酸及其测得的CCS之间的这种联系具有巨大的潜力，可以提高蛋白质鉴定的置信度。由于肽CCS值完全取决于其线性氨基酸序列，因此它们应具有高度准确性，我们的深度学习模型即使对于先前未观察到的肽也可以准确预测CCS值。我们从五种生物的全蛋白蛋白摘要中获取了数据，这导致了超过200万CCS值的测量，其中包括约500,000个独特的肽，这是迄今为止最全面的CCS数据集。”
马蒂亚斯·曼恩（Matthias Mann）教授补充说：“源代码已公开可用，因此可以加速进一步的发展，以进行人类肽宇宙的培训和预测模型。从概念上讲，我们的CCS模型可以通过减少生成库的努力来更快，更便宜。此外，预测的CCS值应允许使用社区图书馆，例如Pan Human Library，一个超过10,000种人类蛋白质的存储库，用于靶向蛋白质组学。”
法比安·蒂斯（Fabian Theis）教授说：“深度学习，尤其是使用的复发性神经网络需要很多样本才能预测，所以当马蒂亚斯（Matthias）接近我并且我们能够共同预测和插值基于肽的生化特性时，我感到非常高兴。他们的序列。我个人喜欢这样一个事实，即我们可以为许多从未有过测量的肽的CCS值估算。”
Bruker蛋白质组学副总裁Gary Kruppa博士评论说：“本文展示了在无偏见的深4D蛋白质组学中，TIMS-PASEF方法准确的CCS值的巨大潜力。经过验证的鲁棒性，更高的吞吐量和超高灵敏度timsTOFplatform is highly suitable for translational research. Large-scale peptide CCS values provide a fundamental advantage in the confidence of protein identification and quantitation in biomarker research in large cohort studies. Furthermore, the benefits of CCS values for improving confidence of identification are also applicable to other multiomics timsTOF workflows, such as metabolomics, lipidomics and glycomics. These are exciting times for our rapidly growing timsTOF user community.”
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1Meier，F.，Köhler，n.d.，Brunner，AD。et al.深入学习肽宇宙的碰撞横截面从一百万个实验值中。纳特社区12, 1185 (2021).https://doi.org/10.1038/s41467-021-21352-8
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