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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

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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.

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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.

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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.

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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.

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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

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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.

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Correspondence to Jeffery W. Kelly.

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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.

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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

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