Motional clustering in supra-τc conformational exchange influences NOE cross-relaxation rate
Artikel i vetenskaplig tidskrift, 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


Christopher Kolloff

Biozentrum University of Basel

Chalmers, Data- och informationsteknik, Data Science och AI

Adam Mazur

Biozentrum University of Basel

Jan K. Marzinek

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

Peter J. Bond

Universiti Kebangsaan Singapura (NUS)

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

Simon Olsson

Chalmers, Data- och informationsteknik, Data Science och AI

Sebastian Hiller

Biozentrum University of Basel

Journal of Magnetic Resonance

1090-7807 (ISSN)

Vol. 338 107196



Teoretisk kemi

Sannolikhetsteori och statistik



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