Enhancing Protein Solubility via Glycosylation: From Chemical Synthesis to Machine Learning Predictions.

Glycosylation is a valuable tool for modulating protein solubility; however, the lack of reliable research strategies has impeded efficient progress in understanding and applying this modification. This study aimed to bridge this gap by investigating the solubility of a model glycoprotein molecule, the carbohydrate-binding module (CBM), through a two-stage process. In the first stage, an approach involving chemical synthesis, comparative analysis, and molecular dynamics simulations of a library of glycoforms was employed to elucidate the effect of different glycosylation patterns on solubility and the key factors responsible for the effect. In the second stage, a predictive mathematical formula, innovatively harnessing machine learning algorithms, was derived to relate solubility to the identified key factors and accurately predict the solubility of the newly designed glycoforms. Demonstrating feasibility and effectiveness, this two-stage approach offers a valuable strategy for advancing glycosylation research, especially for the discovery of glycoforms with increased solubility.

Insights into the effect of protein glycosylation on carbohydrate substrate binding.

The role of glycosylation in the binding of glycoproteins to carbohydrate substrates has not been well understood. The present study addresses this knowledge gap by elucidating the links between the glycosylation patterns of a model glycoprotein, a Family 1 carbohydrate-binding module (TrCBM1), and the thermodynamic and structural properties of its binding to different carbohydrate substrates using isothermal titration calorimetry and computational simulation. The variations in glycosylation patterns cause a gradual transition of the binding to soluble cellohexaose from an entropy-driven process to an enthalpy-driven one, a trend closely correlated with the glycan-induced shift of the predominant binding force from hydrophobic interactions to hydrogen bonding. However, when binding to a large surface of solid cellulose, glycans on TrCBM1 have a more dispersed distribution and thus have less adverse impact on the hydrophobic interaction forces, leading to overall improved binding. Unexpectedly, our simulation results also suggest an evolutionary role of O-mannosylation in transforming the substrate binding features of TrCBM1 from those of type A CBMs to those of type B CBMs. Taken together, these findings provide new fundamental insights into the molecular basis of the role of glycosylation in protein-carbohydrate interactions and are expected to better facilitate further studies in this area.