Protein-based QM-CGMM.

Area of Science:

• Computational Chemistry
• Biophysics
• Molecular Dynamics

Background:

• Accurate molecular simulations require balancing computational cost and accuracy.
• Coarse-graining (CG) methods reduce complexity but often sacrifice thermodynamic fidelity.
• Protein backbones present unique challenges for CG modeling due to their electrostatic contributions.

Purpose of the Study:

• To develop a thermodynamically consistent coarse-graining approach for protein backbones in hybrid QM/MM simulations.
• To accurately represent the electrostatic field of amino acid residues in a CG model.
• To enable efficient calculation of free energy barriers in enzymatic reactions.

Main Methods:

• Developed a 4-bead per amino acid residue model to replicate electrostatic fields.
• Integrated the CG model with a heterogeneous Elastic Network Model (heteroENM) force field.
• Applied Lennard-Jones parameters at the QM/CGMM boundary.
• Utilized Transition-Tempered metadynamics (TTMetaD) for free energy calculations.

Main Results:

• Successfully applied the CGMM approach to the Chorismate Mutase system.
• Obtained a converged Potential of Mean Force (PMF) for the enzymatic reaction.
• The calculated free energy barrier closely matched experimental values.
• Demonstrated that CGMM can reproduce all-atom thermodynamic properties efficiently.

Conclusions:

• The proposed CGMM approach offers a computationally efficient yet accurate method for simulating protein systems.
• This method holds significant potential for studying complex enzymatic mechanisms and other biomolecular processes.
• The thermodynamic consistency of the CGMM model is validated by its ability to predict experimental free energy barriers.