Nakai-Kasai Ayano, Ogata Kosuke, Ishihama Yasushi, Tanaka Toshiyuki
Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Aichi, 466-8555, Japan.
Department of Molecular Systems Bioanalysis, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, 606-8501, Japan.
Commun Chem. 2025 May 6;8(1):137. doi: 10.1038/s42004-025-01540-z.
Collision cross section (CCS) of peptide ions provides an important separation dimension in liquid chromatography/tandem mass spectrometry-based proteomics that incorporates ion mobility spectrometry (IMS), and its accurate prediction is the basis for advanced proteomics workflows. This paper describes experimental data and a prediction model for challenging CCS prediction tasks including longer peptides that tend to have higher charge states. The proposed model is based on a pretrained deep protein language model. While the conventional prediction model requires training from scratch, the proposed model enables training with less amount of time owing to the use of the pretrained model as a feature extractor. Results of experiments with the novel experimental data show that the proposed model succeeds in drastically reducing the training time while maintaining the same or even better prediction performance compared with the conventional method. Our approach presents the possibility of prediction on the basis of "greener" manner training of various peptide properties in proteomic liquid chromatography/tandem mass spectrometry experiments.
肽离子的碰撞横截面（CCS）在结合离子淌度谱（IMS）的基于液相色谱/串联质谱的蛋白质组学中提供了一个重要的分离维度，其准确预测是先进蛋白质组学工作流程的基础。本文描述了针对具有挑战性的CCS预测任务的实验数据和预测模型，这些任务包括往往具有更高电荷态的较长肽段。所提出的模型基于预训练的深度蛋白质语言模型。与传统预测模型需要从头开始训练不同，由于使用预训练模型作为特征提取器，所提出的模型能够在更短的时间内完成训练。对新实验数据的实验结果表明，与传统方法相比，所提出的模型在大幅减少训练时间的同时，保持了相同甚至更好的预测性能。我们的方法为在蛋白质组学液相色谱/串联质谱实验中以“更绿色”的方式训练各种肽特性进行预测提供了可能性。
