Probing Methyl Group Dynamics in Proteins by NMR Cross-Correlated Dipolar Relaxation and Molecular Dynamics Simulations

Nuclear magnetic resonance (NMR) spin relaxation is the most informative approach to experimentally probe the internal dynamics of proteins on the picosecond to nanosecond time scale. At the same time, molecular dynamics (MD) simulations of biological macromolecules are steadily improving through better physical models, enhanced sampling methods, and increased computational power, and they provide exquisite information about flexibility and its role in protein stability and molecular interactions. Many examples have shown that MD is now adept in probing protein backbone motion, but improvements are still required toward a quantitative description of the dynamics of side chains, for example, probed by the dynamics of methyl groups. Thus far, the comparison of computation with experiment for side chain dynamics has primarily focused on the relaxation of 13C and 2H nuclei induced by autocorrelated variation of spin interactions. However, the cross-correlation of 13C-1H dipolar interactions in methyl groups offers an attractive alternative. Here, we establish a computational framework to extract cross correlation relaxation parameters of methyl groups in proteins from all-atom MD simulations. To demonstrate the utility of the approach, cross-correlation relaxation rates of ubiquitin are computed from MD simulations performed with the AMBER99SB*- ILDN and CHARMM36 force fields. Simulation results were found to agree well with those obtained by experiment. Moreover, the data obtained with the two force fields are highly consistent.

Automated summary

Nuclear magnetic resonance (NMR) spin relaxation and molecular dynamics (MD) simulations are important methods for studying the internal dynamics of proteins. This study establishes a computational framework to extract cross correlation relaxation parameters of methyl groups in proteins from MD simulations and successfully obtains results consistent with experimental data.