• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

翻译后修饰肽的色谱保留时间预测。

Chromatographic retention time prediction for posttranslationally modified peptides.

机构信息

Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.

出版信息

Proteomics. 2012 Apr;12(8):1151-9. doi: 10.1002/pmic.201100386.

DOI:10.1002/pmic.201100386
PMID:22577017
Abstract

Retention time prediction of peptides in liquid chromatography has proven to be a valuable tool for mass spectrometry-based proteomics, especially in designing more efficient procedures for state-of-the-art targeted workflows. Additionally, accurate retention time predictions can also be used to increase confidence in identifications in shotgun experiments. Despite these obvious benefits, the use of such methods has so far not been extended to (posttranslationally) modified peptides due to the absence of efficient predictors for such peptides. We here therefore describe a new retention time predictor for modified peptides, built on the foundations of our existing Elude algorithm. We evaluated our software by applying it on five types of commonly encountered modifications. Our results show that Elude now yields equally good prediction performances for modified and unmodified peptides, with correlation coefficients between predicted and observed retention times ranging from 0.93 to 0.98 for all the investigated datasets. Furthermore, we show that our predictor handles peptides carrying multiple modifications as well. This latest version of Elude is fully portable to new chromatographic conditions and can readily be applied to other types of posttranslational modifications. Elude is available under the permissive Apache2 open source License at http://per-colator.com or can be run via a web-interface at http://elude.sbc.su.se.

摘要

在基于质谱的蛋白质组学中,预测液相色谱中肽的保留时间已被证明是一种非常有用的工具,特别是在设计更高效的最新靶向工作流程程序时。此外,准确的保留时间预测还可以用于提高 shotgun 实验鉴定的可信度。尽管有这些明显的好处,但由于缺乏针对此类肽的有效预测器,此类方法迄今为止尚未扩展到(翻译后)修饰肽。因此,我们在这里描述了一种新的修饰肽保留时间预测器,该预测器建立在我们现有的 Elude 算法的基础上。我们通过将其应用于五种常见的修饰类型来评估我们的软件。我们的结果表明,Elude 现在对修饰和未修饰的肽都能产生同样好的预测性能,所有研究数据集的预测和观察到的保留时间之间的相关系数范围从 0.93 到 0.98。此外,我们还表明,我们的预测器可以处理携带多个修饰的肽。Elude 的最新版本完全可移植到新的色谱条件下,并可轻松应用于其他类型的翻译后修饰。Elude 可在 http://per-colator.com 下的宽松 Apache2 开源许可证下使用,也可以通过 http://elude.sbc.su.se 的网络界面运行。

相似文献

1
Chromatographic retention time prediction for posttranslationally modified peptides.翻译后修饰肽的色谱保留时间预测。
Proteomics. 2012 Apr;12(8):1151-9. doi: 10.1002/pmic.201100386.
2
Training, selection, and robust calibration of retention time models for targeted proteomics.靶向蛋白质组学中保留时间模型的训练、选择和稳健校准。
J Proteome Res. 2010 Oct 1;9(10):5209-16. doi: 10.1021/pr1005058.
3
Information-dependent LC-MS/MS acquisition with exclusion lists potentially generated on-the-fly: case study using a whole cell digest of Clostridium thermocellum.基于信息的 LC-MS/MS 采集与实时生成的排除列表:以热纤梭菌全细胞消化物为例的研究
Proteomics. 2012 Apr;12(8):1160-9. doi: 10.1002/pmic.201100425.
4
POSTMan (POST-translational modification analysis), a software application for PTM discovery.POSTMan(翻译后修饰分析),一款用于发现翻译后修饰的软件应用程序。
Proteomics. 2009 Mar;9(5):1400-6. doi: 10.1002/pmic.200800500.
5
Extended Range Proteomic Analysis (ERPA): a new and sensitive LC-MS platform for high sequence coverage of complex proteins with extensive post-translational modifications-comprehensive analysis of beta-casein and epidermal growth factor receptor (EGFR).扩展范围蛋白质组学分析(ERPA):一种新型且灵敏的液相色谱-质谱平台,用于对具有广泛翻译后修饰的复杂蛋白质进行高序列覆盖——β-酪蛋白和表皮生长因子受体(EGFR)的综合分析。
J Proteome Res. 2005 Jul-Aug;4(4):1155-70. doi: 10.1021/pr050113n.
6
Highly multiplexed targeted proteomics using precise control of peptide retention time.利用肽保留时间的精确控制进行高度多重靶向蛋白质组学分析。
Proteomics. 2012 Apr;12(8):1122-33. doi: 10.1002/pmic.201100533.
7
OpenMS and TOPP: open source software for LC-MS data analysis.OpenMS和TOPP:用于液相色谱-质谱数据分析的开源软件。
Methods Mol Biol. 2010;604:201-11. doi: 10.1007/978-1-60761-444-9_14.
8
Automated 20 kpsi RPLC-MS and MS/MS with chromatographic peak capacities of 1000-1500 and capabilities in proteomics and metabolomics.自动化的20千磅力反相液相色谱-质谱联用仪及串联质谱仪,色谱峰容量为1000 - 1500,具备蛋白质组学和代谢组学分析能力。
Anal Chem. 2005 May 15;77(10):3090-100. doi: 10.1021/ac0483062.
9
An iterative strategy for precursor ion selection for LC-MS/MS based shotgun proteomics.基于液相色谱-串联质谱的鸟枪法蛋白质组学中前体离子选择的迭代策略
J Proteome Res. 2009 Jul;8(7):3239-51. doi: 10.1021/pr800835x.
10
Observed peptide pI and retention time shifts as a result of post-translational modifications in multidimensional separations using narrow-range IPG-IEF.在使用窄范围 IPG-IEF 的多维分离中,观察到翻译后修饰引起的肽 pI 和保留时间偏移。
Amino Acids. 2011 Feb;40(2):697-711. doi: 10.1007/s00726-010-0704-2. Epub 2010 Aug 20.

引用本文的文献

1
Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.深度学习直接从肽序列预测高场不对称波形离子迁移谱(FAIMS)中的非正态传输分布。
Anal Chem. 2025 Feb 4;97(4):2254-2263. doi: 10.1021/acs.analchem.4c05359. Epub 2025 Jan 26.
2
Variability analysis of LC-MS experimental factors and their impact on machine learning.LC-MS 实验因素的可变性分析及其对机器学习的影响。
Gigascience. 2022 Dec 28;12. doi: 10.1093/gigascience/giad096. Epub 2023 Nov 20.
3
Dynamic acylome reveals metabolite driven modifications in .
动态酰基化组揭示了代谢物驱动的修饰……(原文此处不完整)
Front Microbiol. 2022 Nov 7;13:1018220. doi: 10.3389/fmicb.2022.1018220. eCollection 2022.
4
Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry.机器学习模型在从头质谱法中对肽段保留时间和迁移时间预测的评价。
J Proteome Res. 2022 Jul 1;21(7):1736-1747. doi: 10.1021/acs.jproteome.2c00124. Epub 2022 May 26.
5
DeepLC can predict retention times for peptides that carry as-yet unseen modifications.DeepLC可以预测携带尚未见过的修饰的肽段的保留时间。
Nat Methods. 2021 Nov;18(11):1363-1369. doi: 10.1038/s41592-021-01301-5. Epub 2021 Oct 28.
6
[Research progress and application of retention time prediction method based on deep learning].基于深度学习的保留时间预测方法的研究进展与应用
Se Pu. 2021 Mar;39(3):211-218. doi: 10.3724/SP.J.1123.2020.08015.
7
Deep Learning in Proteomics.蛋白质组学中的深度学习。
Proteomics. 2020 Nov;20(21-22):e1900335. doi: 10.1002/pmic.201900335. Epub 2020 Oct 30.
8
Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants.全二维气相色谱保留时间预测辅助未知污染物鉴定。
Anal Bioanal Chem. 2018 Dec;410(30):7931-7941. doi: 10.1007/s00216-018-1415-x. Epub 2018 Oct 25.
9
Peptide Retention in Hydrophilic Strong Anion Exchange Chromatography Is Driven by Charged and Aromatic Residues.肽在亲水强阴离子交换色谱中的保留是由带电和芳香残基驱动的。
Anal Chem. 2018 Apr 3;90(7):4635-4640. doi: 10.1021/acs.analchem.7b05157. Epub 2018 Mar 21.
10
Study of structure-dependent chromatographic behavior of glycopeptides using reversed phase nanoLC.使用反相纳米液相色谱法研究糖肽的结构依赖性色谱行为
Electrophoresis. 2017 Sep;38(17):2193-2199. doi: 10.1002/elps.201600547. Epub 2017 May 17.