Coarse-Grained Model of Glycosaminoglycans for Biomolecular Simulations

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Coarse-Grained Model of Glycosaminoglycans for Biomolecular Simulations
Title:
Coarse-Grained Model of Glycosaminoglycans for Biomolecular Simulations
Journal Title:
Journal of Chemical Theory and Computation
Keywords:
Publication Date:
15 February 2024
Citation:
Shivgan, A. T., Marzinek, J. K., Krah, A., Matsudaira, P., Verma, C. S., & Bond, P. J. (2024). Coarse-Grained Model of Glycosaminoglycans for Biomolecular Simulations. Journal of Chemical Theory and Computation, 20(8), 3308–3321. https://doi.org/10.1021/acs.jctc.3c01088
Abstract:
Proteoglycans contain glycosaminoglycans (GAGs) which are negatively charged linear polymers made of repeating disaccharide units of uronic acid and hexosamine units. They play vital roles in numerous physiological and pathological processes, particularly in governing cellular communication and attachment. Depending on their sulfonation state, acetylation, and glycosidic linkages, GAGs belong to different families. The high molecular weight, heterogeneity, and flexibility of GAGs hamper their characterization at atomic resolution, but this may be circumvented via coarse-grained (CG) approaches. In this work, we report a CG model for a library of common GAG types in their isolated or proteoglycan-linked states compatible with version 2.2 (v2.2) of the widely popular CG Martini force field. The model reproduces conformational and thermodynamic properties for a wide variety of GAGs, as well as matching structural and binding data for selected proteoglycan test systems. The parameters developed here may thus be employed to study a range of GAG-containing biomolecular systems, thereby benefiting from the efficiency and broad applicability of the Martini framework.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Young Individual Research Grant (YIRG)
Grant Reference no. : A2084c0160

This research / project is supported by the A*STAR - ID HTPO Seed Fund
Grant Reference no. : C211418001

This research / project is supported by the Ministry of Education - Academic Research Fund (AcRF) Tier 3
Grant Reference no. : MOE2012-T3-1-008

This research is supported by core funding from: Bioinformatics Institute (BII)
Grant Reference no. : N.A
Description:
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see doi.org/10.1021/acs.jctc.3c01088
ISSN:
1549-9626
1549-9618
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