CIBR: Full-Spectrum Prediction of Peptide Tandem Mass Spectra using Deep Neural Networks

CIBR：使用深度神经网络对肽串联质谱进行全谱预测

• 批准号: 2011271
• 负责人: Haixu Tang
• 金额: $ 78.1万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011271&HistoricalAwards=false
• 关键词: CIBR; Full; Spectrum; Prediction; Peptide

The last decade has witnessed rapid advances in mass spectrometry (MS) technology. In particular, the liquid chromatograph coupled tandem mass spectrometry (LC-MS/MS) has become a popular analytical tool for characterizing complex protein samples in many branches of life sciences, including microbiology, environmental science, plant biology, agriculture and biomedicine. This project aims to exploit publicly available proteomic data for predicting tandem mass (MS/MS) spectra of peptides. Successful prediction of peptide MS/MS spectra is of great theoretical interests (for better understanding mechanisms of peptide fragmentation in mass spectrometers), and will significantly improve the peptide identification, which is critical for the analyses of complex protein samples. The PIs of this project are actively involved and lead some the school and departments outreach activities and events, including the annual summer camp for girl scouts. They plan to recruit students from HBCU institutes to participate summer research each year in this project.The PIs of the project propose to a sequence-to-sequence (seq2seq) deep learning model for predicting the full MS/MS spectra of peptides directly from their sequences without any assumption of fragmentation rules. They will also exploit the multitask learning (MTL) approach for predicting the MS/MS spectra from peptides containing post-translation modification (PTMs), and the MS/MS spectra acquired by using different ion activation methods, such as Electron Transfer Dissociation (ETDs). The deep learning models will be implemented and released in open source software tools to be used by the research community. The update of the research project will be made available through the project website: http://www.predfull.com.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

过去十年见证了质谱（MS）技术的快速发展。特别是，液相色谱耦合串联质谱（LC-MS/MS）已成为一种流行的分析工具，用于表征复杂的蛋白质样品在生命科学的许多分支，包括微生物学，环境科学，植物生物学，农业和生物医学。本计画的目的是利用公开的蛋白质组学资料来预测多肽的串联质谱。成功预测肽MS/MS谱具有重要的理论意义（为了更好地理解质谱仪中肽片段化的机制），并且将显著改善肽鉴定，这对于复杂蛋白质样品的分析至关重要。该项目的主要负责人积极参与并领导了学校和部门的一些外联活动和活动，包括每年的女童子军夏令营。他们计划每年从HBCU研究所招募学生参与该项目的暑期研究。该项目的PI提出了一个序列到序列（seq 2seq）深度学习模型，用于直接从序列预测肽的完整MS/MS谱，而无需任何碎片规则的假设。他们还将利用多任务学习（MTL）方法来预测含有翻译后修饰（PTM）的肽的MS/MS光谱，以及通过使用不同的离子活化方法（如电子转移解离（ETD））获得的MS/MS光谱。深度学习模型将在开源软件工具中实现和发布，供研究社区使用。研究项目的更新将通过项目网站提供：http://www.predfull.com.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data.

Metaprod：多重蛋白质组学和元蛋白质组学数据的高度可配合的质谱分析仪。

• DOI: 10.1021/acs.jproteome.2c00614
• 发表时间: 2023-02-03
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Canderan, Jamie;Stamboulian, Moses;Ye, Yuzhen
• 通讯作者: Ye, Yuzhen