MD2NMR: Linking molecular dynamics with NMR relaxation

Area of Science:

• Computational Chemistry
• Biophysics
• Molecular Dynamics

Background:

• Accurate prediction of NMR relaxation parameters from MD simulations is crucial for linking molecular motions to experimental data.
• Existing methods may lack efficiency or broad applicability for diverse biomolecular systems.

Purpose of the Study:

• Introduce MD2NMR, an open-source Python framework for calculating NMR relaxation parameters (R1, R2, τc) from MD trajectories.
• Provide a flexible and user-friendly tool for validating MD simulations.

Main Methods:

• MD2NMR implements efficient algorithms for time correlation and spectral density functions, accounting for global and internal motions.
• The framework supports multiple trajectory formats and user-defined parameters.
• Calculations include longitudinal (R1) and transverse (R2) relaxation rates, and rotational correlation times (τc).

Main Results:

• MD2NMR accurately reproduces experimental NMR relaxation behavior for ubiquitin, GB1, GB3, and histone H3/H4 tails.
• Achieved high Pearson correlation coefficients (up to 0.89) and low RMSE (0.75) for small systems.
• Demonstrated good accuracy for histone H3 tail rotational correlation times (PCC up to 0.79, RMSE 6.08) with computational efficiency.

Conclusions:

• MD2NMR offers a robust platform for validating MD simulations by accurately predicting NMR relaxation parameters.
• The framework's flexibility and accuracy make it extensible to various biomolecular systems.
• MD2NMR is freely available as an open-source Python package, promoting wider adoption and research.