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

Area of Science:

• Biochemistry and Biophysics
• Computational Chemistry
• Materials Science

Background:

• Protein stability is crucial for drug development and function.
• Ionic liquids (ILs) show promise as protein stabilizers due to biocompatibility and solubility.
• Mechanisms of ILs in protein stabilization, especially hydrogen bonding and interfacial effects, require further elucidation.

Purpose of the Study:

• To elucidate the mechanism by which ionic liquids (ILs) enhance membrane protein stability.
• To develop a predictive model for protein stability using machine learning.
• To quantitatively assess the impact of hydrogen bonds and interfacial structures on protein stability.

Main Methods:

• A machine learning framework integrating molecular docking, unsupervised learning, and molecular dynamics simulations.
• Correlation analysis to understand IL-protein interactions.
• Supervised learning for predictive model development and interpretability analysis.

Main Results:

• Ionic liquids form clusters adsorbed on protein surfaces, entering hydration layers.
• Intermolecular hydrogen bonds between ILs and proteins were observed, enhancing stability.
• A predictive model for protein stability was successfully established and verified.

Conclusions:

• Ionic liquids enhance membrane protein stability through surface adsorption and hydrogen bond formation.
• The study provides quantitative insights into IL-protein interactions and the role of interfacial structures.
• The developed framework aids in the rational design of novel protein stabilizers.