The impact of glycosylation on the conformational ensembles of β-, δ-, and γ-sarcoglycans
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Abstract
Glycosylation is a pivotal post-translational modification that influences protein folding, stability, and interactions with direct implications for muscular dystrophy pathogenesis and emerging gene therapies. Sarcoglycans (SGs), β, δ, γ, and α subunits of the dystrophin-glycoprotein complex (DGC), contain essential N-linked glycosylation sites, and mutations disrupt glycan attachment, destabilize the complex, and cause limb-girdle muscular dystrophy. However, the structural consequences of SG glycosylation remain poorly defined due to the absence of experimental SG complex structures. Here, we use homology modeling, AlphaFold predictions, and all-atom molecular-dynamics simulations to probe how N-linked glycans reshape the conformational ensembles of β-, δ-, and γ-SG monomers and the β-δ-γ heterotrimer core. We find that glycosylation increases flexibility and conformational heterogeneity in isolated monomers but reinforces a compact, stabilized architecture in the heterotrimer. Contact-map and clustering analyses show that glycans redistribute local residue interactions while preserving global trimer organization, suggesting a context-dependent role in destabilizing monomers yet reinforcing complex stability. These findings provide the first atomistic insight into how glycosylation primes SGs for assembly and may explain why mutations at glycosylation sites disrupt complex integrity and drive muscular dystrophy phenotypes.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · cited 2× · see citing works
“Guided by these homologs, the amino acid sequences of β -, δ -, and γ -SGs were extracted from UniProt 19 (see Table S1) and modeled as both monomers and a heterotrimer using AlphaFold 20,21 and homology modeling via the SWISS-MODEL server.”
Molecular dynamics with coupling to an external bath
Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984) · The Journal of Chemical Physics · doi:10.1063/1.448118 · Cited by 30,626 · see citing works
Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 29,588 · see citing works
SWISS-MODEL: homology modelling of protein structures and complexes
Waterhouse, A. M., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T., Rempfer, C., Bordoli, L., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky427 · Cited by 13,005 · see citing works
CHARMM‐GUI: A web‐based graphical user interface for CHARMM
Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20945 · Cited by 9,284 · see citing works
CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins
Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. (2016) · Nature Methods · doi:10.1038/nmeth.4067 · Cited by 6,708 · see citing works
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · cited 1× · see citing works
“Guided by these homologs, the amino acid sequences of β -, δ -, and γ -SGs were extracted from UniProt 19 (see Table S1) and modeled as both monomers and a heterotrimer using AlphaFold 20,21 and homology modeling via the SWISS-MODEL server.”
UniProt: the universal protein knowledgebase
The UniProt Consortium (2018) · Nucleic Acids Research · doi:10.1093/nar/gky092 · Cited by 2,043 · see citing works
CHARMM-GUI Membrane Builder for mixed bilayers and its application to yeast membranes.
Jo, S., Lim, J. B., Klauda, J. B., & Im, W. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.04.013 · Cited by 1,670 · see citing works
Scalable Parallel Programming with CUDA
Nickolls, J. R., Buck, I., Garland, M., & Kevin Skadron, Y. (2008) · Queue · doi:10.1145/1365490.1365500 · Cited by 1,666 · see citing works
Membrane organization of the dystrophin-glycoprotein complex
Ervasti, J. M., & Campbell, K. P. (1991) · Cell · doi:10.1016/0092-8674(91)90035-w · Cited by 1,332 · see citing works
Automated Builder and Database of Protein/Membrane Complexes for Molecular Dynamics Simulations
Jo, S., Kim, T., & Im, W. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000880 · Cited by 1,186 · see citing works
OPM: Orientations of Proteins in Membranes database
Lomize, M. A., Lomize, A. L., Pogozheva, I. D., & Mosberg, H. I. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btk023 · Cited by 1,178 · see citing works
Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike Protein
Casalino, L., Gaieb, Z., Goldsmith, J. A., Hjorth, C. K., Dommer, A. C., Harbison, A. M., Fogarty, C. A., de Barros, E. P., Taylor, B. C., McLellan, J. S., et al. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c01056 · Cited by 909 · see citing works
SHIFTX2: significantly improved protein chemical shift prediction
Han, B., Liu, Y., Ginzinger, S., & Wishart, D. S. (2011) · Journal of Biomolecular NMR · doi:10.1007/s10858-011-9478-4 · Cited by 673 · see citing works
Effect of glycosylation on protein folding: A close look at thermodynamic stabilization
Shental-Bechor, D., & Levy, Y. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801340105 · Cited by 619 · see citing works
Glycoprotein Complex Anchoring Dystrophin to Sarcolemma1
Yoshida, M., & Ozawa, E. (1990) · The Journal of Biochemistry · doi:10.1093/oxfordjournals.jbchem.a123276 · Cited by 505 · see citing works
Mutations in the Dystrophin-Associated Protein γ-Sarcoglycan in Chromosome 13 Muscular Dystrophy
Noguchi, S., McNally, E. M., Othmane, K. B., Hagiwara, Y., Mizuno, Y., Yoshida, M., Yamamoto, H., Bönnemann, C. G., Gussoni, E., Denton, P. H., et al. (1995) · Science · doi:10.1126/science.270.5237.819 · Cited by 503 · see citing works
Lee, J., Cheng, X., Jo, S., MacKerell, A. D., Klauda, J. B., & Im, W. (2016) · Biophysical Journal · doi:10.1016/j.bpj.2015.11.3431 · Cited by 453 · see citing works
The Role of Protein Loops and Linkers in Conformational Dynamics and Allostery
Papaleo, E., Saladino, G., Lambrughi, M., Lindorff‐Larsen, K., Gervasio, F. L., & Nussinov, R. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00623 · Cited by 426 · see citing works
Effect of N-linked glycosylation on glycopeptide and glycoprotein structure
Imperiali, B., & O'Connor, S. E. (1999) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(99)00021-6 · Cited by 387 · see citing works
Efficient Algorithms for Langevin and DPD Dynamics
Goga, N., Rzepiela, A. J., de Vries, A. H., Marrink, . J., & Berendsen, H. J. C. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct3000876 · Cited by 337 · see citing works
ProMod3—A versatile homology modelling toolbox
Studer, G., Tauriello, G., Bienert, S., Biasini, M., Johner, N. B., & Schwede, T. F. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008667 · Cited by 300 · see citing works
Glycoproteins: glycan presentation and protein-fold stability
Wormald, M. R., & Dwek, R. A. (1999) · Structure · doi:10.1016/s0969-2126(99)80095-1 · Cited by 298 · see citing works
Lee, H. S., Qi, Y., & Im, W. (2015) · Scientific Reports · doi:10.1038/srep08926 · Cited by 290 · see citing works
Folding of glycoproteins: toward understanding the biophysics of the glycosylation code
Shental-Bechor, D., & Levy, Y. (2009) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2009.07.002 · Cited by 251 · see citing works
Durbeej, M., Cohn, R. D., Hrstka, R. F., Moore, S. A., Allamand, V., Davidson, B. L., Williamson, R. A., & Campbell, K. P. (2000) · Molecular Cell · doi:10.1016/s1097-2765(00)80410-4 · Cited by 209 · see citing works
Molecular Organization of Sarcoglycan Complex in Mouse Myotubes in Culture
Chan, Y.-M. M., Bönnemann, C. G., Lidov, H. G. W., & Kunkel, L. M. (1998) · The Journal of Cell Biology · doi:10.1083/jcb.143.7.2033 · Cited by 135 · see citing works
Assembly of the Sarcoglycan Complex
Holt, K. H., & Campbell, K. P. (1998) · Journal of Biological Chemistry · doi:10.1074/jbc.273.52.34667 · Cited by 131 · see citing works
Solá, R. J., Rodríguez‐Martínez, J. A., & Griebenow, K. H. (2007) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-007-6551-y · Cited by 120 · see citing works
Impact of sarcoglycan complex on mechanical signal transduction in murine skeletal muscle
Barton, E. R. (2005) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00192.2005 · Cited by 83 · see citing works
Specific assembly pathway of sarcoglycans is dependent on beta‐ and delta‐sarcoglycan
Shi, W., Chen, Z., Schottenfeld, J., Stahl, R. C., Kunkel, L. M., & Chan, Y.-M. M. (2004) · Muscle & Nerve · doi:10.1002/mus.10566 · Cited by 64 · see citing works
The sarcoglycan complex in skeletal muscle
Berger, J. (2015) · Frontiers in bioscience · doi:10.2741/4418 · Cited by 53 · see citing works
Klem, H., Hocky, G. M., & McCullagh, M. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01290 · Cited by 49 · see citing works
Self-citationStructure and assembly of the dystrophin glycoprotein complex
Wan, L., Ge, X., Xu, Q., Huang, G., Yang, T., Campbell, K. P., Zhen, Y., & Wu, J. (2024) · Nature · doi:10.1038/s41586-024-08310-2 · Cited by 36 · see citing works
Chen, J., Shi, W., Zhang, Y., Sokol, R. G., Cai, H., Lun, M., Moore, B. F., Farber, M. J., Stepanchick, J. S., Bönnemann, C. G., et al. (2006) · Experimental Cell Research · doi:10.1016/j.yexcr.2006.01.024 · Cited by 36 · see citing works
Effect of glycosylation on protein folding: From biological roles to chemical protein synthesis
Hao, C., Zou, Q., Bai, X., & Shi, W.-W. (2025) · iScience · doi:10.1016/j.isci.2025.112605 · Cited by 33 · see citing works
Finding the Sweet Spot: Assembly and Glycosylation of the Dystrophin‐Associated Glycoprotein Complex
Townsend, D. (2014) · The Anatomical Record · doi:10.1002/ar.22974 · Cited by 32 · see citing works
Native DGC structure rationalizes muscular dystrophy-causing mutations
Liu, S., Su, T., Xia, X., & Zhou, Z. H. (2024) · Nature · doi:10.1038/s41586-024-08324-w · Cited by 24 · see citing works
Campbell, M. D., Witcher, M., Gopal, A., & Michele, D. E. (2016) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00521.2015 · Cited by 17 · see citing works
Yanaka, S., Sakae, Y., Miyanoiri, Y., Yamaguchi, T., Isono, Y., Kondo, S., Iwasaki, M., Onitsuka, M., Yagi, H., & Kato, K. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2505473122 · Cited by 13 · see citing works
Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM
Sasmal, S., Pal, T., Hocky, G. M., & McCullagh, M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00223 · Cited by 12 · see citing works
Self-citationJia, Y., Liu, Y., Wang, Y., Li, J., & Li, G. (2024) · Chemical Science · doi:10.1039/d4sc03672g · Cited by 12 · see citing works
N-glycosylation modulates enzymatic activity of Trypanosoma congolense trans-sialidase
Rosenau, J., Grothaus, I. L., Yang, Y., Kumar, N. D., Ciacchi, L. C., Kelm, S., & Waespy, M. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102403 · Cited by 9 · see citing works
Tutorial on quantifying and sampling biomolecular ensembles with ShapeGMM
Sasmal, S., McCullagh, M., & Hocky, G. M. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0305455 · Cited by 2 · see citing works
Self-citationIn Vitro Glycosylation of the Membrane Protein γ-Sarcoglycan in Nanodiscs
Harris, M. S., Dolan, R. F., Bryce, J. R., Ewusi, J. G., & Cook, G. A. (2023) · ACS Omega · doi:10.1021/acsomega.3c06135 · Cited by 2 · see citing works
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Flagged first, then by citations
- Authors
- Fazelpour, E., Cook, G. A., & McCullagh, M.
- Affiliations
- Oklahoma State University (US)
- Corresponding author
- McCullagh, Martin
- Venue
- Biophysical Journal
- Publisher
- Elsevier BV
- Published
- 2026-06
- Volume
- 125
- Issue
- 11
- Pages
- 2657-2669
- ISSN
- 0006-3495 (ISSN-L)
- PMC
- PMC13294987
- OpenAlex
- W7155102384
- Cited by
- 0
- References
- 46
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biophysical Journal (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion, cc-by-nd)
- Funders
- National Science Foundation (OAC-1531128)
- National Institute of Allergy and Infectious Diseases Division of Intramural Research (R01AI166050)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Glycosylation and Glycoproteins Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
- Keywords
- sarcoglycans, glycosylation, N-glycans, dystrophin-glycoprotein complex, limb-girdle muscular dystrophy, conformational ensembles, protein flexibility, glycosylation site mutations
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