Motional clustering in supra-τc conformational exchange influences NOE cross-relaxation rate
Journal article, 2022

Biomolecular spin relaxation processes, such as the NOE, are commonly modeled by rotational τc-tumbling combined with fast motions on the sub-τc timescale. Motions on the supra-τc timescale, in contrast, are considered to be completely decorrelated to the molecular tumbling and therefore invisible. Here, we show how supra-τc dynamics can nonetheless influence the NOE build-up between methyl groups. This effect arises because supra-τc motions can cluster the fast-motion ensembles into discrete states, affecting distance averaging as well as the fast-motion order parameter and hence the cross-relaxation rate. We present a computational approach to estimate methyl–methyl cross-relaxation rates from extensive (>100×τc) all-atom molecular dynamics (MD) trajectories on the example of the 723-residue protein Malate Synthase G. The approach uses Markov state models (MSMs) to resolve transitions between metastable states and thus to discriminate between sub-τc and supra-τc conformational exchange. We find that supra-τc exchange typically increases NOESY cross-peak intensities. The methods described in this work extend the theory of modeling sub-μs dynamics in spin relaxation and thus contribute to a quantitative estimation of NOE cross-relaxation rates from MD simulations, eventually leading to increased precision in structural and functional studies of large proteins.

Protein dynamics

Nuclear Overhauser Effect

Malate Synthase G

Slow conformational exchange

Methyl spectroscopy

Molecular dynamics simulations

Author

Christopher Kolloff

Biozentrum University of Basel

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Adam Mazur

Biozentrum University of Basel

Jan K. Marzinek

Agency for Science, Technology and Research (A*STAR)

Peter J. Bond

National University of Singapore (NUS)

Agency for Science, Technology and Research (A*STAR)

Simon Olsson

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Sebastian Hiller

Biozentrum University of Basel

Journal of Magnetic Resonance

1090-7807 (ISSN) 10960856 (eISSN)

Vol. 338 107196

Subject Categories

Biophysics

Theoretical Chemistry

Probability Theory and Statistics

DOI

10.1016/j.jmr.2022.107196

PubMed

35367892

More information

Latest update

4/14/2022