Insights into ionic liquid-enhanced membrane protein stability through machine learning and molecular simulation

Abstract

Protein stability plays a critical role in structural elucidation, enzyme activity, and the storage of protein drugs, where ionic liquids (ILs) have emerged as promising protein stabilizers due to their exceptional biocompatibility and superior solubility. However, the underlying mechanisms by which ILs modulate protein stability, particularly through the regulation of hydrogen bonding and interfacial structures, remain inadequately understood. Herein, a machine learning-based framework, integrating molecular docking, unsupervised learning, molecular dynamics simulations and correlation analysis, is applied to clarify the mechanism of ILs enhancing membrane protein stability. It is found that ILs form clusters that adsorbed on the protein surface, with ILs entering the hydration layer of protein and forming intermolecular hydrogen bonds with the protein surface, thereby improving stability, consistent with experiments. Furthermore, a predictive model for protein stability is established by supervised learning, and verification of the mechanism through interpretability analysis. Our framework quantitatively reveals the influence of hydrogen bonds and interface structures on membrane protein stability. Overall, these quantitative results not only deepen our understanding of the interactions between ILs and protein but also shed light on the rational design of protein stabilizers.