瑞典哥德堡大学Daniel Bojar研究组开发出新方法通过深度学习从串联质谱中预测聚糖结构。2024年7月1日，《自然—方法学》杂志在线发表了这项成果。

在新整理的500000条注释串联质谱（MS/MS）图谱集上进行训练，研究人员报道了CandyCrunch，这是一种扩张残差神经网络，可在数秒内从原始液相色谱–MS/MS数据中预测糖结构（最高准确率为90.3%）。研究人员开发了一个基于Python的开放式工作流程，其中包括原始数据转换和预测，然后是自动整理和片段注释，预测结果再现并扩展了专家注释。研究人员展示了这一流程可用于全新注释、诊断片段鉴定和高通量糖组学。

为了达到最大效果，整个流程与该糖工作平台紧密衔接，可在https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb上轻松测试。研究人员希望CandyCrunch能够使结构糖学和阐明聚糖生物学作用的工作平民化。

研究人员表示，糖构成了最复杂的翻译后修饰，调节着蛋白质在健康和疾病中的活性。然而，MS/MS数据的结构注释是糖组学研究的一个瓶颈，阻碍了高通量研究的进行，使糖组学研究只能由少数专家完成。

附：英文原文

Title: Predicting glycan structure from tandem mass spectrometry via deep learning

Author: Urban, James, Jin, Chunsheng, Thomsson, Kristina A., Karlsson, Niclas G., Ives, Callum M., Fadda, Elisa, Bojar, Daniel

Issue&Volume: 2024-07-01

Abstract: Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography–MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb. We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

DOI: 10.1038/s41592-024-02314-6

Source: https://www.nature.com/articles/s41592-024-02314-6