Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics.

Area of Science:

• Computational Biology
• Structural Biology
• Biophysics

Background:

• Proteins adopt diverse conformations beyond their crystal structures, crucial for function but often computationally expensive to study.
• Molecular dynamics (MD) simulations can reveal these alternative states but face high computational costs.

Purpose of the Study:

• To develop and apply a novel statistical mechanics and AI-based MD framework for enhanced sampling of protein loop conformations.
• To investigate the conformational dynamics and stability of T4 lysozyme mutants.

Main Methods:

• Utilized a hybrid framework combining statistical mechanics and artificial intelligence for enhanced MD sampling.
• Applied the framework to study three mutants of T4 lysozyme, focusing on protein loop dynamics.
• Analyzed reaction coordinates to understand conformational flexibility changes.

Main Results:

• Successfully ranked T4 lysozyme mutants based on their excited-state stability.
• Gained insights into sequence perturbations driving significant variations in conformational flexibility.
• Demonstrated accurate comparison of loop conformation populations with reduced bias.

Conclusions:

• The developed framework efficiently samples protein loop conformations, overcoming high computational costs.
• Provides valuable insights into structure-function relationships by elucidating conformational dynamics.
• Applicable to a broad range of macromolecules in biological and chemical sciences.