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

Autor:	Onigbinde S; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA., Gutierrez Reyes CD; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA., Sandilya V; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA., Chukwubueze F; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA., Oluokun O; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA., Sahioun S; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA., Oluokun A; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA., Mechref Y; Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA.
Jazyk:	angličtina
Zdroj:	Expert review of proteomics [Expert Rev Proteomics] 2024 Oct 31, pp. 1-32. Date of Electronic Publication: 2024 Oct 31.
DOI:	10.1080/14789450.2024.2418491
Abstrakt:	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.
Databáze:	MEDLINE
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