Narrowing the gap between experimental and computational determination of methyl group dynamics in proteins

Abstract

Nuclear magnetic resonance (NMR) spin relaxation has become the mainstay technique to sample protein dynamics at atomic resolution, expanding its repertoire from backbone 15N to side-chain 2H probes. At the same time, molecular dynamics (MD) simulations have become increasingly powerful to study protein dynamics due to steady improvements of physical models, algorithms, and computational power. Good agreement between generalized Lipari-Szabo order parameters derived from experiment and MD simulation has been observed for the backbone dynamics of a number of proteins. However, the agreement for the more dynamic side-chains, as probed by methyl group relaxation, was much worse. Here, we use T4 lysozyme (T4L), a protein with moderate tumbling anisotropy, to showcase a number of improvements that reduce this gap by a combined evaluation of NMR relaxation experiments and MD simulations. By applying a protein force field with accurate methyl group rotation barriers in combination with a solvation model that yields correct protein rotational diffusion times, we find that properly accounting for anisotropic protein tumbling is an important factor to improve the match between NMR and MD in terms of methyl axis order parameters, spectral densities, and relaxation rates. The best agreement with the experimentally measured relaxation rates is obtained by a posteriori fitting the appropriate internal time correlation functions, truncated by anisotropic overall tumbling. In addition, MD simulations led us to account for a hitherto unrealized artifact in deuterium relaxation experiments arising from strong coupling for leucine residues in uniformly 13C-enriched proteins. For T4L, the improved analysis reduced the RMSD between MD and NMR derived methyl axis order parameters from 0.19 to 0.11. At the level of the spectral density functions, the improvements allow us to extract the most accurate parameters that describe protein side-chain dynamics. Further improvement is challenging not only due to force field and sampling limitations in MD, but also due to inherent limitations of the Lipari-Szabo model to capture complex dynamics.

Predicting NMR Relaxation of Proteins from Molecular Dynamics Simulations with Accurate Methyl Rotation Barriers

“…Notably, the RMSD between R(D z ) from MD and NMR of about 7 s −1 for ubiquitin (Table 5) is small compared to the absolute values and in a similar range as the RMSD obtained for T4 lysozyme in our previous work, due to the reparametrization of the methyl rotation barriers. 14,15 Our results emphasize that still further improvements of biomolecular force fields are needed in order to achieve a similar correlation between experimental and computational relaxation rates for methyl groups as is routinely obtained for backbone amide bonds.…”

“…for R(D z ) of methyl groups in T4 lysozyme, 15 showing that also fast dynamics as probed by R(D z ) can in principle be described reliably in MD simulations. Notably, the RMSD between R(D z ) from MD and NMR of about 7 s −1 for ubiquitin (Table 5) is small compared to the absolute values and in a similar range as the RMSD obtained for T4 lysozyme in our previous work, due to the reparametrization of the methyl rotation barriers.…”

“…The time scale of rotational diffusion in MD simulations strongly depends on the water model. 15,56,62,[64][65][66][67] While the water model usually does not have a strong effect on the dynamics of backbone N-H bonds, it changes the time scale of overall rotational tumbling and therefore the spectral densities. Table 3 summarizes the rotational diffusion constants D iso obtained from our simulations with the different water models.…”

“…However, a comparably reliable prediction of dynamical parameters of side-chains by MD is more demanding. 7,10,15,[33][34][35] This is especially the case for the motions of methyl groups in side-chains because their internal dynamics results from the convolution of fast and slow dynamics on time scales that range from ps to many ns and sometimes beyond.…”

Localized and Collective Motions in HET‐s(218‐289) Fibrils from Combined NMR Relaxation and MD Simulation

“…We suspect this is a near‐universal problem when complex motions are involved, brought about by fitting a correlation function with fewer terms than are present in the real motion. Further evidence of biasing is provided in the Supporting Information, Section 4, where we find that an MD trajectory yields good reproduction of experimental R 1 rate constants at several magnetic fields, but fitting of NMR data and MD‐derived correlation functions separately to multi‐exponential functions yields very different parameters (see for similar results for methyl dynamics).…”