Predicting NMR relaxation of proteins from molecular dynamics simulations with accurate methyl rotation barriers

19Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The internal dynamics of proteins occurring on time scales from picoseconds to nanoseconds can be sensitively probed by nuclear magnetic resonance (NMR) spin relaxation experiments, as well as by molecular dynamics (MD) simulations. This complementarity offers unique opportunities, provided that the two methods are compared at a suitable level. Recently, several groups have used MD simulations to compute the spectral density of backbone and side chain molecular motions and to predict NMR relaxation rates from these. Unfortunately, in the case of methyl groups in protein side chains, inaccurate energy barriers to methyl rotation were responsible for a systematic discrepancy in the computed relaxation rates, as demonstrated for the AMBER ff99SB*-ILDN force field (and related parameter sets), impairing quantitative agreement between simulations and experiments. However, correspondence could be regained by emending the MD force field with accurate coupled cluster quantum chemical calculations. Spurred by this positive result, we tested whether this approach could be generally applicable, in spite of the fact that different MD force fields employ different water models. Improved methyl group rotation barriers for the CHARMM36 and AMBER ff15ipq protein force fields were derived, such that the NMR relaxation data obtained from the MD simulations even now display very good agreement with the experiment. Results herein showcase the performance of present-day MD force fields and manifest their refined ability to accurately describe internal protein dynamics.

Cite

CITATION STYLE

APA

Hoffmann, F., Mulder, F. A. A., & Schäfer, L. V. (2020). Predicting NMR relaxation of proteins from molecular dynamics simulations with accurate methyl rotation barriers. Journal of Chemical Physics, 152(8). https://doi.org/10.1063/1.5135379

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free