Enhanced Sampling of Protein Conformational Transitions via Dynamically Optimized Collective Variables

Area of Science:

• Biophysics
• Computational Biology
• Protein Dynamics

Background:

• Protein conformational transitions are crucial for function but often involve slow dynamics.
• Understanding these transitions provides mechanistic insight and aids enhanced sampling methods.
• Identifying slow degrees of freedom is essential for efficient simulation.

Purpose of the Study:

• To investigate the conformational transition of L99A T4 Lysozyme.
• To implement a variational approach to conformational dynamics metadynamics.
• To identify slow modes governing protein transitions using combined experimental and simulation data.

Main Methods:

• Utilized a variational approach to conformational dynamics metadynamics.
• Integrated data from Nuclear Magnetic Resonance (NMR) experiments.
• Incorporated information from short Molecular Dynamics (MD) simulations.
• Applied these methods to the L99A T4 Lysozyme protein.

Main Results:

• Successfully identified slow modes of the protein system.
• Revealed the presence of two distinct intermediate states during the conformational transition.
• Achieved these findings at an affordable computational cost.

Conclusions:

• The implemented variational approach effectively characterizes protein conformational dynamics.
• The study identified key intermediate states in L99A T4 Lysozyme.
• This method accelerates the understanding of protein dynamics and enhances sampling techniques.