Conformational Sampling by Ab Initio Molecular Dynamics Simulations Improves NMR Chemical Shift Predictions

Area of Science:

• Computational Chemistry
• Molecular Dynamics Simulations
• Nuclear Magnetic Resonance Spectroscopy

Background:

• Accurate prediction of Nuclear Magnetic Resonance (NMR) chemical shifts is crucial for understanding molecular structure and dynamics.
• Classical molecular dynamics (MD) simulations often struggle to accurately reproduce experimental NMR chemical shifts, particularly for amide protons.
• Amide groups are fundamental components of protein backbones, making their accurate simulation important for structural biology.

Purpose of the Study:

• To evaluate the accuracy of Car-Parrinello molecular dynamics (CPMD) simulations in predicting NMR chemical shifts for N-methyl acetamide.
• To investigate the impact of conformational sampling and explicit solvent molecules on the accuracy of calculated NMR chemical shifts.
• To explore the potential of this approach for validating molecular force fields.

Main Methods:

• Car-Parrinello molecular dynamics (CPMD) simulations were performed on N-methyl acetamide as a model system.
• NMR chemical shifts were calculated from the ensembles generated by CPMD simulations.
• Explicit solvent molecules were included in the simulations to mimic physiological conditions.

Main Results:

• CPMD simulations, incorporating conformational sampling and explicit solvent, achieved excellent agreement with experimental NMR chemical shifts.
• A significant improvement was observed compared to classical MD simulations, especially for amide protons which were previously predicted at incorrect high-field shifts.
• The enhanced accuracy is attributed to the simulation's ability to capture shorter hydrogen bonds and internal solute degrees of freedom.

Conclusions:

• Car-Parrinello molecular dynamics is a powerful tool for accurately predicting NMR chemical shifts in systems with amide groups.
• Accounting for conformational flexibility and explicit solvent interactions is essential for high-fidelity chemical shift prediction.
• The proposed approach offers a valuable method for validating force fields by identifying structural origins of simulation-experiment discrepancies.