Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers.

IF 3.8 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Expert Review of Proteomics Pub Date : 2024-10-31 DOI:10.1080/14789450.2024.2418491
Sherifdeen Onigbinde, Cristian D Gutierrez Reyes, Vishal Sandilya, Favour Chukwubueze, Odunayo Oluokun, Sarah Sahioun, Ayobami Oluokun, Yehia Mechref
{"title":"Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers.","authors":"Sherifdeen Onigbinde, Cristian D Gutierrez Reyes, Vishal Sandilya, Favour Chukwubueze, Odunayo Oluokun, Sarah Sahioun, Ayobami Oluokun, Yehia Mechref","doi":"10.1080/14789450.2024.2418491","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.</p><p><strong>Areas covered: </strong>This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.</p><p><strong>Expert opinion: </strong>The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.</p>","PeriodicalId":50463,"journal":{"name":"Expert Review of Proteomics","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Review of Proteomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1080/14789450.2024.2418491","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction: The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

Areas covered: This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

Expert opinion: The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
优化糖肽富集技术以鉴定临床生物标记物。
引言:通过 LC-MS/MS 和先进的富集技术对糖肽进行鉴定和表征对于推动临床糖蛋白组学的发展至关重要,这将对疾病生物标记物和治疗靶点的发现产生重大影响。尽管在富集方法方面取得了进展,如凝集素亲和层析(LAC)、亲水作用液相层析(HILIC)和静电排斥亲水作用层析(ERLIC),但特异性、效率和可扩展性方面的问题依然存在,阻碍了对疾病理解至关重要的复杂糖基化模式的彻底分析:本综述探讨了糖肽富集和质谱分析目前面临的挑战和创新解决方案,强调了新型材料和计算技术的进步对提高灵敏度和特异性的重要性。它概述了这些技术在临床糖蛋白组学中的潜在未来发展方向,强调了它们对医学诊断和治疗策略的变革性影响:金属有机框架(MOFs)、共价有机框架(COFs)、功能纳米材料和在线富集等创新材料的应用有望通过提供选择性更强、更稳健的富集平台,解决与糖蛋白组学分析相关的挑战。此外,人工智能和机器学习的整合正在给糖蛋白组学带来革命性的变化,它能加强对 LC-MS/MS 大量数据的处理和解读,促进生物标记物的发现,提高预测的准确性,从而为个性化医疗提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Expert Review of Proteomics
Expert Review of Proteomics 生物-生化研究方法
CiteScore
7.60
自引率
0.00%
发文量
20
审稿时长
6-12 weeks
期刊介绍: Expert Review of Proteomics (ISSN 1478-9450) seeks to collect together technologies, methods and discoveries from the field of proteomics to advance scientific understanding of the many varied roles protein expression plays in human health and disease. The journal coverage includes, but is not limited to, overviews of specific technological advances in the development of protein arrays, interaction maps, data archives and biological assays, performance of new technologies and prospects for future drug discovery. The journal adopts the unique Expert Review article format, offering a complete overview of current thinking in a key technology area, research or clinical practice, augmented by the following sections: Expert Opinion - a personal view on the most effective or promising strategies and a clear perspective of future prospects within a realistic timescale Article highlights - an executive summary cutting to the author''s most critical points.
期刊最新文献
Protein biomarkers for subtyping breast cancer and implications for future research: a 2024 update. Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers. Glycosylation in cancer as a source of biomarkers. Eleven shades of PASEF. Understanding metabolic resistance strategy of clinically isolated antibiotic-resistant bacteria by proteomic approach.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1