Computing the Rotational Diffusion of Biomolecules via Molecular Dynamics Simulation and Quaternion Orientations

Area of Science:

• Computational biophysics
• Molecular dynamics simulations
• Protein dynamics

Background:

• Rotational diffusion (D_rot) is crucial for understanding biomolecular properties.
• All-atom simulations for D_rot prediction are computationally expensive and limited by water models.

Purpose of the Study:

• To evaluate coarse-grained force fields, specifically MARTINI with elastic networks (EN), for predicting D_rot.
• To develop and apply a quaternion-based method for calculating D_rot orientation.
• To establish guidelines for simulation sampling and reporting for accurate D_rot prediction.

Main Methods:

• Utilized the MARTINI force field with elastic networks for coarse-grained molecular dynamics simulations.
• Employed a quaternion-based approach to compute D_rot from rotation autocorrelations.
• Simulated over 2 μs for >10 proteins (5-157 kDa) and analyzed convergence.
• Compared simulation results with experimental data and all-atom simulations (CHARMM22*/TIP3p).

Main Results:

• MARTINI+EN accurately predicted isotropic D_rot within 10-20% of experimental values.
• Convergence requires sampling >50 × τ_theor.
• Anisotropic tumbling fluctuations impact precision; axial decomposition (D_x, D_y, D_z) is recommended for comparability.
• MARTINI+EN D_rot orientations closely matched experimental and all-atom data for well-ordered proteins.

Conclusions:

• Coarse-grained simulations with MARTINI+EN provide a viable and efficient method for predicting biomolecular rotational diffusion.
• Adequate sampling and reporting of axial D_rot components are essential for reliable predictions.
• This approach facilitates future ab initio prediction of NMR relaxation by integrating coarse-grained and all-atom dynamics.