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Stereoelectronic Effects in Stabilizing Protein–N-Glycan Interactions Revealed by Experiment and Machine Learning

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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… Click to show full 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 R 2 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. Analysis of the thermodynamics of protein– N -glycan interactions perturbed by mutations has revealed an enthalpy–entropy compensation that depends on the electronics of the interacting side chains. Machine-learned and statistical models showed that protein– N -glycan interactions highly correlate with stereoelectronic effects, and that a major part of protein– N -glycan interactions can be explained using the energetic rules of frontier molecular orbital interactions.

Keywords: machine learning; glycan interactions; stereoelectronic effects; stabilizing protein; protein glycan

Journal Title: Nature chemistry
Year Published: 2021

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