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

Abstract

The energetics of protein-carbohydrate interactions, central to many life processes, cannot yet be predictably manipulated. 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 significantly 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 non-linear 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, 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.

Continuous Homogeneous Catalytic Oxidation of C–H Bonds by Metal-Free Carbon Dots with a Poly(ascorbic acid) Structure

“…A more detailed regulation of feature division is shown in Table S1. Pearson’s correlation coefficients − of these seven features are shown in Figure b, suggesting that they are independent enough to the following training set. Further, the XGBoost model (XGB) was selected from four classical models as the training and test model to understand the nonlinear correlation between selected features and results based on the calculation results shown in Figures S2 and S3.…”

“…In this work, ML is used to predict the homogeneous cyclohexane oxidation by the carbon-based catalyst as well as the reaction conditions. The schematic illustrations for present ML studies are shown in Figure a and Figure S1. − The data set of cyclohexane oxidation was obtained from the literature and notebooks in our laboratory. It contains 652 items, including 113 positive samples (17.3%) and 539 negative samples (82.7%).…”

“…With the help of machine learning (ML), − we predicted and fabricated the metal-free C-Dots, with a well-defined ascorbic acid unit based polymer-like structure, as homogeneous catalysts for C–H bond oxidation, by which one-step and highly selective oxidation from cyclohexane to AA was achieved in a homogeneous system with acetone as the solvent. After the reaction, the product of AA will be separated by crystallization when the solution temperature comes down to room temperature, while the C-Dots remain in the solution.…”

Profiling Genetic Diversity Reveals the Molecular Basis for Balancing Function with Misfolding in Alpha-1 Antitrypsin

“…Interestingly, there is a glycosylation site on Asn 107 in this region, suggesting that the N-glycan dependent pathway may be uniquely poised for sensing functional-structural relationships in that region guiding its stability during nascent synthesis 11, 27, 28, 63, 64 . This conclusion is consistent with results that have demonstrated the importance of ERManI as the sole known genetic modifier in AATD 28 .…”

Advances in mass spectrometry‐based glycoproteomics: An update covering the period 2017–2021

“…Glycoproteomics is a proteomics division dedicate to identifying and characterizing the glycans and glycoproteins in a given cell or tissue. The glycan moieties modulate and control important biological functions, including cell-to-cell recognition, cell adhesion, immune cell trafficking, protein folding, solubility, and stability [9][10][11][12][13][14][15][16][17]. Additionally, aberrant glycosylation has been correlated with mammalian disease and hereditary disorders, such as immune deficiencies, cardiovascular disease, and cancer [1,3,[18][19][20][21][22][23][24][25].…”