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Progress in the pretreatment and analysis of carbohydrates in food: An update since 2013. J Chromatogr A 2021; 1655:462496. [PMID: 34492577 DOI: 10.1016/j.chroma.2021.462496] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 08/21/2021] [Accepted: 08/22/2021] [Indexed: 11/21/2022]
Abstract
Carbohydrates in foods and other matrices plays vital roles in their diverse biological functions. Carbohydrates serve not only as functional substances but also as structural materials, such as components of membranes, and participate in cellular recognition. The fact that carbohydrates are indispensable has contributed to the need for pretreatment and analytical methods to be developed for their characterization. The aim of this review is to provide a comprehensive overview of carbohydrate pretreatment and determination methods in various matrices. The pretreatment methods include simple and more developed approaches (e.g., solid phase extraction, supercritical fluid extraction, and different microextraction methods, among others). The analytical methods include those by liquid chromatography (including high-performance anion-exchange chromatography), capillary electrophoresis, gas chromatography and supercritical fluid chromatography, and others. Different pretreatment methods and determination approaches are updated, compared, and discussed. Moreover, we discuss and compare the strengths and weaknesses of different methods and suggest their future prospects.
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Scherbinina SI, Toukach PV. Three-Dimensional Structures of Carbohydrates and Where to Find Them. Int J Mol Sci 2020; 21:E7702. [PMID: 33081008 PMCID: PMC7593929 DOI: 10.3390/ijms21207702] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.
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Affiliation(s)
- Sofya I. Scherbinina
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
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Barnett CB, Senapathi T, Naidoo KJ. Comparative ligand structural analytics illustrated on variably glycosylated MUC1 antigen-antibody binding. Beilstein J Org Chem 2020; 16:2540-2550. [PMID: 33133286 PMCID: PMC7590620 DOI: 10.3762/bjoc.16.206] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/30/2020] [Indexed: 01/03/2023] Open
Abstract
When faced with the investigation of the preferential binding of a series of ligands against a known target, the solution is not always evident from single structure analysis. An ensemble of structures generated from computer simulations is valuable; however, visual analysis of the extensive structural data can be overwhelming. Rapid analysis of trajectory data, with tools available in the Galaxy platform, can be used to understand key features and compare differences that inform the preferential ligand structure that favors binding. We illustrate this informatics approach by investigating the in-silico binding of a peptide and glycopeptide epitope of the glycoprotein Mucin 1 (MUC1) binding with the antibody AR20.5. To study the binding, we performed molecular dynamics simulations using OpenMM and then used the Galaxy platform for data analysis. The same analysis tools are applied to each of the simulation trajectories and this process was streamlined by using Galaxy workflows. The conformations of the antigens were analyzed using root-mean-square deviation, end-to-end distance, Ramachandran plots, and hydrogen bonding analysis. Additionally, RMSF and clustering analysis were carried out. These analyses were used to rapidly assess key features of the system, interrogate the dynamic structure of the ligand, and determine the role of glycosylation on the conformational equilibrium. The glycopeptide conformations in solution change relative to the peptide; thus a partially pre-structuring is seen prior to binding. Although the bound conformation of peptide and glycopeptide is similar, the glycopeptide fluctuates less and resides in specific conformers for more extended periods. This structural analysis which gives a high-level view of the features in the system under observation, could be readily applied to other binding problems as part of a general strategy in drug design or mechanistic analysis.
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Affiliation(s)
- Christopher B Barnett
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch, 7701, South Africa
| | - Tharindu Senapathi
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch, 7701, South Africa
| | - Kevin J Naidoo
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch, 7701, South Africa.,Infectious Disease and Molecular Medicine, Faculty of Health Science, University of Cape Town, Rondebosch, 7701, South Africa
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Everest-Dass AV, Moh ESX, Ashwood C, Shathili AMM, Packer NH. Human disease glycomics: technology advances enabling protein glycosylation analysis - part 1. Expert Rev Proteomics 2018; 15:165-182. [PMID: 29285957 DOI: 10.1080/14789450.2018.1421946] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
INTRODUCTION Protein glycosylation is recognized as an important post-translational modification, with specific substructures having significant effects on protein folding, conformation, distribution, stability and activity. However, due to the structural complexity of glycans, elucidating glycan structure-function relationships is demanding. The fine detail of glycan structures attached to proteins (including sequence, branching, linkage and anomericity) is still best analysed after the glycans are released from the purified or mixture of glycoproteins (glycomics). The technologies currently available for glycomics are becoming streamlined and standardized and many features of protein glycosylation can now be determined using instruments available in most protein analytical laboratories. Areas covered: This review focuses on the current glycomics technologies being commonly used for the analysis of the microheterogeneity of monosaccharide composition, sequence, branching and linkage of released N- and O-linked glycans that enable the determination of precise glycan structural determinants presented on secreted proteins and on the surface of all cells. Expert commentary: Several emerging advances in these technologies enabling glycomics analysis are discussed. The technological and bioinformatics requirements to be able to accurately assign these precise glycan features at biological levels in a disease context are assessed.
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Affiliation(s)
- Arun V Everest-Dass
- a Biomolecular Discovery and Design Research Centre, Faculty of Science and Engineering , Macquarie University , Sydney , Australia.,b Institute for Glycomics , Griffith University , Gold Coast , Australia.,c ARC Centre for Nanoscale BioPhotonics , Macquarie University , Sydney , Australia
| | - Edward S X Moh
- a Biomolecular Discovery and Design Research Centre, Faculty of Science and Engineering , Macquarie University , Sydney , Australia.,c ARC Centre for Nanoscale BioPhotonics , Macquarie University , Sydney , Australia
| | - Christopher Ashwood
- a Biomolecular Discovery and Design Research Centre, Faculty of Science and Engineering , Macquarie University , Sydney , Australia.,c ARC Centre for Nanoscale BioPhotonics , Macquarie University , Sydney , Australia
| | - Abdulrahman M M Shathili
- a Biomolecular Discovery and Design Research Centre, Faculty of Science and Engineering , Macquarie University , Sydney , Australia.,c ARC Centre for Nanoscale BioPhotonics , Macquarie University , Sydney , Australia
| | - Nicolle H Packer
- a Biomolecular Discovery and Design Research Centre, Faculty of Science and Engineering , Macquarie University , Sydney , Australia.,b Institute for Glycomics , Griffith University , Gold Coast , Australia.,c ARC Centre for Nanoscale BioPhotonics , Macquarie University , Sydney , Australia
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