XMECP: Reaching State-of-the-Art MECP Optimization in Multiscale Complex Systems

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Biochemistry

Background:

• Minimum Energy Crossing Points (MECs) are crucial for understanding photochemical and enzymatic reactions.
• Accurate MECP calculations are challenging in complex multiscale systems.
• Existing methods may not fully capture non-adiabatic effects or contributions from molecular mechanics (MM) regions.

Purpose of the Study:

• Introduce XMECP, a novel Python program for robust and efficient MECP optimization.
• Demonstrate XMECP's capabilities in investigating diverse chemical and biochemical systems.
• Highlight the importance of derivative coupling vectors (DCVs) and non-quantum mechanics (QM) residue contributions.

Main Methods:

• Developed and applied the XMECP program for MECP calculations.
• Utilized QM/MM (Quantum Mechanics/Molecular Mechanics) methods.
• Investigated systems including benzophenone photosensitization, DNA base pair electron transfer, iron-containing oxygenase, and flavoprotein photochemistry.
• Employed branching plane updating algorithms for MECP optimization with explicit DCV calculation.

Main Results:

• XMECP successfully optimized MECPs in various complex systems.
• Demonstrated that non-QM residues can significantly influence DCVs, impacting nonadiabatic and spin-orbit coupling.
• Showcased noncovalent interactions' role in DCV influence on QM regions.
• Identified a key arginine residue's significant contribution to spin-orbit coupling in Fe/2OGX-catalyzed oxygen activation.

Conclusions:

• XMECP provides a powerful platform for state-of-the-art MECP optimization in multiscale systems.
• The study emphasizes the critical role of non-QM residues and DCVs in understanding reaction mechanisms.
• Revealed unexpected but significant contributions of specific residues to enzymatic processes, advancing mechanistic insights.