通过深度学习,从双重质谱法预测甘氨酸结构
James Urban1,2, Chunsheng Jin3, Kristina A Thomsson3
1Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
Nature methods
|July 1, 2024
概括
新的人工智能工具CandyCrunch从质谱数据快速预测糖结构. 糖化学的这一突破加速了研究,并有助于理解糖甘在健康和疾病中的作用.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 葡萄糖科学 (Glycoscience) 是一种科学.
背景情况:
- 甘氨酸是关键的翻译后修改,影响各种生物过程中的蛋白质功能.
- 使用双重质谱法 (MS/MS) 进行甘氨酸的结构注释是甘氨酸学研究中的一个重大瓶.
- 目前的方法限制了高吞吐量甘氨酸化合物,需要专门的专业知识.
研究的目的:
- 开发一种自动化和快速的方法来从MS/MS数据中预测糖结构.
- 为了克服草图中手动注释的局限性.
- 为了使高吞吐量甘氨基化合物和民主化该领域.
主要方法:
- 开发CandyCrunch,这是一个扩展的残留神经网络,在50万注释的MS/MS光谱上训练.
- 创建一个基于Python的开放访问工作流,用于原始数据转换,预测和自动化策划.
- 与 glycowork 平台的集成,以提高可用性和可访问性.
主要成果:
- 糖果Crunch在几秒钟内从原始液体染色学-MS/MS数据中预测糖结构,达到最高准确度 (90.3%).
- 工作流成功地总结和扩展了专家的注释.
- 在de novo注释,诊断片段识别和高通量糖化合物中证明有用.
结论:
- 糖果Crunch显著加快了从MS/MS数据中的甘氨酸结构阐明.
- 开发的工作流程使结构性糖性成分民主化,使其更广泛的研究社区可以访问.
- 该工具有助于更深入地了解甘氨酸在健康和疾病中的生物作用.
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