category
Nature Methods
date
Mar 23, 2026
slug
status
Published
summary
创新性地整合碰撞诱导碎裂、电子诱导碎裂和光子诱导碎裂三种技术到标准LC-MS流程，通过深度学习模型跨方法预测质谱谱图，显著提升蛋白质鉴定的覆盖率和自动化水平
tags
蛋白质组学
测序技术
type
Post
📄 原文题目
Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage
🔗 原文链接
💡 AI 核心解读
创新性地整合碰撞诱导碎裂、电子诱导碎裂和光子诱导碎裂三种技术到标准LC-MS流程，通过深度学习模型跨方法预测质谱谱图，显著提升蛋白质鉴定的覆盖率和自动化水平
📝 英文原版摘要
<p>Nature Methods, Published online: 23 March 2026; <a href="https://www.nature.com/articles/s41592-026-03042-9">doi:10.1038/s41592-026-03042-9</a></p>An integrated mass spectrometry platform enabling automated collision-, electron- and photon-based fragmentation techniques is presented. A deep learning-based model trained to predict spectra across all dissociation methods further enhances protein identification.
- 作者:NotionNext
- 链接:https://tangly1024.com/article/32c48bd6-1f96-81d8-b9f6-f87acdf5c580
- 声明:本文采用 CC BY-NC-SA 4.0 许可协议，转载请注明出处。
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