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Published December 26, 2017 | Supplemental Material + Published
Journal Article Open

Predicting glycosaminoglycan surface protein interactions and implications for studying axonal growth

Abstract

Cell-surface carbohydrates play important roles in numerous biological processes through their interactions with various protein-binding partners. These interactions are made possible by the vast structural diversity of carbohydrates and the diverse array of carbohydrate presentations on the cell surface. Among the most complex and important carbohydrates are glycosaminoglycans (GAGs), which display varied stereochemistry, chain lengths, and patterns of sulfation. GAG–protein interactions participate in neuronal development, angiogenesis, spinal cord injury, viral invasion, and immune response. Unfortunately, little structural information is available for these complexes; indeed, for the highly sulfated chondroitin sulfate motifs, CS-E and CS-D, there are no structural data. We describe here the development and validation of the GAG-Dock computational method to predict accurately the binding poses of protein-bound GAGs. We validate that GAG-Dock reproduces accurately (<1-Å rmsd) the crystal structure poses for four known heparin–protein structures. Further, we predict the pose of heparin and chondroitin sulfate derivatives bound to the axon guidance proteins, protein tyrosine phosphatase σ (RPTPσ), and Nogo receptors 1–3 (NgR1-3). Such predictions should be useful in understanding and interpreting the role of GAGs in neural development and axonal regeneration after CNS injury.

Additional Information

© 2017 National Academy of Sciences. Published under the PNAS license. Contributed by William A. Goddard III, November 15, 2017 (sent for review August 25, 2017; reviewed by Michael L. Klein and Jim C. Paulson). Published online before print December 11, 2017. This work was supported by National Institutes of Health (NIH) Grants R01 GM084724 (to L.C.H.-W.) and 5T32 GM07616 (to C.J.R. and G.M.M.). Initial support for this work was from NIH Grants R01-NS071112, R01-NS073115, and R01-AI040567 (to A.R.G., R.A., and W.A.G.), and support from National Science Foundation (NSF) Emerging Frontiers in Research and Innovation (EFRI)-Origami Design for Integration of Self-assembling Systems for Engineering Innovation (ODISSEI) Grant 1332411 was used to complete the project. The computers used in this research were funded by grants from the Defense University Research Instrument Program (to W.A.G.) and from NSF Materials Research Science and Engineering Center (MRSEC), Center for the Science and Engineering of Materials (CSEM) (equipment part of the NSF-MRSEC-CSEM). Author contributions: A.R.G. and W.A.G. designed research; A.R.G., C.J.R., and G.M.M. performed research; A.R.G., C.J.R., G.M.M., R.A., and W.A.G. analyzed data; and A.R.G., C.J.R., G.M.M., L.C.H.-W., and W.A.G. wrote the paper. Reviewers: M.L.K., Temple University; and J.C.P., The Scripps Research Institute. The authors declare no conflict of interest. This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1715093115/-/DCSupplemental.

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Published - PNAS-2017-Griffith-13697-702.pdf

Supplemental Material - pnas.1715093115.sapp.pdf

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August 21, 2023
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