Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Stereoelectronic effects in stabilizing protein–N-glycan interactions revealed by experiment and machine learning

Nature Chemistry volume 13, pages 480–487 (2021)Cite this article

Subjects

Abstract

The energetics of protein–carbohydrate interactions, central to many life processes, cannot yet be manipulated predictably. This is mostly due to an incomplete quantitative understanding of the enthalpic and entropic basis of these interactions in aqueous solution. Here, we show that stereoelectronic effects contribute to stabilizing protein–N-glycan interactions in the context of a cooperatively folding protein. Double-mutant cycle analyses of the folding data from 52 electronically varied N-glycoproteins demonstrate an enthalpy–entropy compensation depending on the electronics of the interacting side chains. Linear and nonlinear models obtained using quantum mechanical calculations and machine learning explain up to 79% and 97% of the experimental interaction energy variability, as inferred from the R2 value of the respective models. Notably, the protein–carbohydrate interaction energies strongly correlate with the molecular orbital energy gaps of the interacting substructures. This suggests that stereoelectronic effects must be given a greater weight than previously thought for accurately modelling the short-range dispersive van der Waals interactions between the N-glycan and the protein.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A strategy combining experiment (grey trajectory) and theory (red trajectory) enables probing of the thermodynamic and electronic origins of protein–carbohydrate interactions.
Fig. 2: An expanded repertoire of electronically varied N-glycosylated proteins constructed using chemical incorporation of natural and unnatural amino acids.
Fig. 3: The effect of N-glycosylation on the temperature-induced unfolding profile depends on the chemical identity of the sugar and amino acid at the interaction site.
Fig. 4: The thermodynamic origin of ΔΔGglyc differs among the electronically varied WW glycovariants.
Fig. 5: Machine learning extracts the stereoelectronic factors that explain the stabilizing protein–N-glycan interactions.
Fig. 6: Interactions between molecular orbitals contribute to the stabilizing effect of N-glycosylation.

Similar content being viewed by others

Data availability

All data generated or analysed during this study are included in this paper and its Supplementary Information.

Code availability

All of the analysis done in this study was carried out using previously published codes. No custom code was generated during the current study.

References

  1. Hebert, D. N., Lamriben, L., Powers, E. T. & Kelly, J. W. The intrinsic and extrinsic effects of N-linked glycans on glycoproteostasis. Nat. Chem. Biol. 10, 902–910 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Varki, A. Biological roles of glycans. Glycobiology 27, 3–49 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Banks, D. D. The effect of glycosylation on the folding kinetics of erythropoietin. J. Mol. Biol. 412, 536–550 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Lynch, C. J. & Lane, D. A. N-linked glycan stabilization of the VWF A2 domain. Blood 127, 1711–1718 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ressler, V. T. & Raines, R. T. Consequences of the endogenous N-glycosylation of human ribonuclease 1. Biochemistry 58, 987–996 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tan, N. Y. et al. Sequence-based protein stabilization in the absence of glycosylation. Nat. Commun. 5, 3099 (2014).

    Article  PubMed  Google Scholar 

  7. Yuzwa, S. A. et al. Increasing O-GlcNAc slows neurodegeneration and stabilizes tau against aggregation. Nat. Chem. Biol. 8, 393–399 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Chang, M. M. et al. Small-molecule control of antibody N-glycosylation in engineered mammalian cells. Nat. Chem. Biol. 15, 730–736 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Elliott, S. et al. Enhancement of therapeutic protein in vivo activities through glycoengineering. Nat. Biotechnol. 21, 414–421 (2003).

    Article  CAS  PubMed  Google Scholar 

  10. Laughrey, Z. R., Kiehna, S. E., Riemen, A. J. & Waters, M. L. Carbohydrate−π interactions: what are they worth? J. Am. Chem. Soc. 130, 14625–14633 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chaffey, P. K. et al. Structural insight into the stabilizing effect of O-glycosylation. Biochemistry 56, 2897–2906 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Chen, M. M. et al. Perturbing the folding energy landscape of the bacterial immunity protein Im7 by site-specific N-linked glycosylation. Proc. Natl Acad. Sci. USA 107, 22528–22533 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Gavrilov, Y., Shental-Bechor, D., Greenblatt, H. M. & Levy, Y. Glycosylation may reduce protein thermodynamic stability by inducing a conformational distortion. J. Phys. Chem. Lett. 6, 3572–3577 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Woods, R. J. Predicting the structures of glycans, glycoproteins and their complexes. Chem. Rev. 118, 8005–8024 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Culyba, E. K. et al. Protein native-state stabilization by placing aromatic side chains in N-glycosylated reverse turns. Science 331, 571–575 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Price, J. L., Powers, D. L., Powers, E. T. & Kelly, J. W. Glycosylation of the enhanced aromatic sequon is similarly stabilizing in three distinct reverse turn contexts. Proc. Natl Acad. Sci. USA 108, 14127–14132 (2011).

    Article  CAS  PubMed  Google Scholar 

  17. Ardejani, M. S., Powers, E. T. & Kelly, J. W. Using cooperatively folded peptides to measure interaction energies and conformational propensities. Acc. Chem. Res. 50, 1875–1882 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gao, J. M., Bosco, D. A., Powers, E. T. & Kelly, J. W. Localized thermodynamic coupling between hydrogen bonding and microenvironment polarity substantially stabilizes proteins. Nat. Struct. Mol. Biol. 16, 684–690 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hudson, K. L. et al. Carbohydrate–aromatic interactions in proteins. J. Am. Chem. Soc. 137, 15152–15160 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hsu, C.-H. et al. The dependence of carbohydrate-aromatic interaction strengths on the structure of the carbohydrate. J. Am. Chem Soc. 138, 7636–7648 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen, W. et al. Structural and energetic basis of carbohydrate–aromatic packing interactions in proteins. J. Am. Chem. Soc. 135, 9877–9884 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. García-Hernández, E. et al. Structural energetics of protein–carbohydrate interactions: insights derived from the study of lysozyme binding to its natural saccharide inhibitors. Protein Sci. 12, 135–142 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Fox, J. M. et al. The molecular origin of enthalpy/entropy compensation in biomolecular recognition. Annu. Rev. Biophys. 47, 223–250 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Krug, R. R., Hunter, W. G. & Grieger, R. A. Statistical interpretation of enthalpy–entropy compensation. Nature 261, 566–567 (1976).

    Article  CAS  Google Scholar 

  25. Qian, H. & Hopfield, J. J. Entropy–enthalpy compensation: perturbation and relaxation in thermodynamic systems. J. Chem. Phys. 105, 9292–9298 (1996).

    Article  CAS  Google Scholar 

  26. Sharp, K. Entropy—enthalpy compensation: fact or artifact? Protein Sci. 10, 661–667 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bigman, L. S. & Levy, Y. Entropy–enthalpy compensation in conjugated proteins. Chem. Phys. 514, 95–105 (2018).

    Article  CAS  Google Scholar 

  28. Grunwald, E. & Steel, C. Solvent reorganization and thermodynamic enthalpy–entropy compensation. J. Am. Chem. Soc. 117, 5687–5692 (1995).

    Article  CAS  Google Scholar 

  29. Hassan, S. A. Implicit treatment of solvent dispersion forces in protein simulations. J. Comput. Chem. 35, 1621–1629 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhong, D., Pal, S. K. & Zewail, A. H. Biological water: a critique. Chem. Phys. Lett. 503, 1–11 (2011).

    Article  CAS  Google Scholar 

  31. Yang, L., Adam, C., Nichol, G. S. & Cockroft, S. L. How much do van der Waals dispersion forces contribute to molecular recognition in solution?. Nat. Chem. 5, 1006–1010 (2013).

    Article  CAS  PubMed  Google Scholar 

  32. Grimme, S. Density functional theory with London dispersion corrections. WIREs Comput. Mol. Sci. 1, 211–228 (2011).

    Article  CAS  Google Scholar 

  33. Hermann, J., DiStasio, R. A. & Tkatchenko, A. First-principles models for van der Waals interactions in molecules and materials: concepts, theory and applications. Chem. Rev. 117, 4714–4758 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Wagner, C. et al. Non-additivity of molecule-surface van der Waals potentials from force measurements. Nat. Commun. 5, 5568 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Frisch, M. J. et al. Gaussian 09, Revision A.2 (Gaussian, 2009).

  36. Pang, S.-K. Quantum-chemically-calculated mechanistically interpretable molecular descriptors for drug-action mechanism study—a case study of anthracycline anticancer antibiotics. RSC Adv. 6, 74426–74435 (2016).

    Article  CAS  Google Scholar 

  37. Lu, T. & Chen, F. Quantitative analysis of molecular surface based on improved marching tetrahedra algorithm. J. Mol. Graph. Model. 38, 314–323 (2012).

    Article  PubMed  Google Scholar 

  38. Murray, J. S. et al. Statistically-based interaction indices derived from molecular surface electrostatic potentials: a general interaction properties function (GIPF). J. Mol. Struct. THEOCHEM 307, 55–64 (1994).

    Article  Google Scholar 

  39. Pham, T.-L. et al. Learning structure–property relationship in crystalline materials: a study of lanthanide–transition metal alloys. J. Chem. Phys. 148, 204106 (2018).

    Article  PubMed  Google Scholar 

  40. Li, J. & Zhang, R.-Q. Strong orbital interaction in a weak CH–π hydrogen bonding system. Sci. Rep. 6, 22304 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Perras, F. A. et al. Observation of CH⋅⋅⋅π interactions between methyl and carbonyl groups in proteins. Angew. Chem. Int. Ed. 56, 7564–7567 (2017).

    Article  CAS  Google Scholar 

  42. Iwata, S. Dispersion energy evaluated by using locally projected occupied and excited molecular orbitals for molecular interaction. J. Chem. Phys. 135, 094101 (2011).

    Article  PubMed  Google Scholar 

  43. Kapuy, E. & Kozmutza, C. Calculation of the dispersion interaction energy by using localized molecular orbitals. J. Chem. Phys. 94, 5565–5573 (1991).

    Article  CAS  Google Scholar 

  44. Ardejani, M. S. & Orner, B. P. Obey the peptide assembly rules. Science 340, 561–562 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Jalali-Heravi, M., Shahbazikhah, P., Zekavat, B. & Ardejani, M. S. Principal component analysis-ranking as a variable selection method for the simulation of 13C nuclear magnetic resonance spectra of xanthones using artificial neural networks. QSAR Comb. Sci. 26, 764–772 (2007).

    Article  CAS  Google Scholar 

  46. Schaftenaar, G. & de Vlieg, J. Quantum mechanical polar surface area. J. Comput. Aided Mol. Des. 26, 311–318 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  48. Plevin, M. J., Bryce, D. L. & Boisbouvier, J. Direct detection of CH/π interactions in proteins. Nat. Chem. 2, 466–471 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Glendening, E. D., Landis, C. R. & Weinhold, F. Natural bond orbital methods. WIREs Comput. Mol. Sci. 2, 1–42 (2012).

    Article  CAS  Google Scholar 

  50. Frisch, M. J. et al. Gaussian 16, Revision C.01 (Gaussian, 2016).

  51. Baerends, E. J. et al. ADF2017 (SCM, Vrije Universiteit, 2014).

  52. Patera, L. L., Queck, F., Scheuerer, P. & Repp, J. Mapping orbital changes upon electron transfer with tunnelling microscopy on insulators. Nature 566, 245–248 (2019).

    Article  CAS  PubMed  Google Scholar 

  53. Bartlett, G. J., Choudhary, A., Raines, R. T. & Woolfson, D. N. n → π* interactions in proteins. Nat. Chem. Biol. 6, 615–620 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was funded by grants GM51105 (J.W.K.) and GM100934 (L.N.) from the National Institutes of Health. The authors thank G. J. Kroon and J. Dyson for their help with the protein NMR experiments, D. E. Mortenson for help with the chemical synthesis experiments, K. N. Houk, M. Jäger and D. E. Mortenson for helpful discussions, J.-C. Ducom and W. Han for help with the computational infrastructure and C. Fearns and K. Lee for critical reading of the manuscript.

Author information

Authors and Affiliations

  1. Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA

    Maziar S. Ardejani, Evan T. Powers & Jeffery W. Kelly

  2. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA

    Louis Noodleman

  3. The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, CA, USA

    Jeffery W. Kelly

Authors
  1. Maziar S. Ardejani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Louis Noodleman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Evan T. Powers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeffery W. Kelly
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S.A., L.N., E.T.P. and J.W.K. conceived and designed the experiments. M.S.A. carried out the experiments and performed the data analysis. M.S.A., L.N., E.T.P. and J.W.K. co-wrote the paper. Correspondence and requests for materials should be addressed to J.W.K.

Corresponding author

Correspondence to Jeffery W. Kelly.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Chemistry thanks Christopher Bauer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–49, Methods and Table 1.

Supplementary Data 1

Experimental values and quantum mechanical descriptors used for machine learning.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ardejani, M.S., Noodleman, L., Powers, E.T. et al. Stereoelectronic effects in stabilizing protein–N-glycan interactions revealed by experiment and machine learning. Nat. Chem. 13, 480–487 (2021). https://doi.org/10.1038/s41557-021-00646-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41557-021-00646-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing