Static solid-state 2H NMR methods in studies of protein side-chain dynamics

Abstract

In this review, we discuss the experimental static deuteron NMR techniques and computational approaches most useful for the investigation of side-chain dynamics in protein systems. Focus is placed on the interpretation of line shape and relaxation data within the framework of motional modeling. We consider both jump and diffusion models and apply them to uncover glassy behaviors, conformational exchange and dynamical transitions in proteins. Applications are chosen from globular and membrane proteins, amyloid fibrils, peptide adsorbed on surfaces and proteins specific to connective tissues.

Basic experiments in 2H static NMR for the characterization of protein side-chain dynamics

“…Static 2 H solid-state NMR has been known for decades as a sensitive tool for studies of the dynamics in systems ranging from polymers to peptides, nucleic acids, and proteins. (1–17)…”

“…Common commercially available labeling patterns of methyl groups are shown in red. Taken from (17), license number 4226710808630.…”

Protein dynamics in the solid-state from 2H NMR lineshape analysis. III. MOMD in the presence of Magic Angle Spinning

“…There are a considerable number of studies reporting on multi-simple-mode analyses of 2 H lineshapes in the absence of MAS [11,41,42]. A more limited number of studies report on the analysis of 2 H lineshapes from polycrystalline samples in the presence of MAS [28–33,36,43].…”

“…Methods for analyzing 2 H lineshapes from polycrystalline samples exist [1–6,11–15]. Traditionally simple, typically jump-type, dynamic modes have been employed [2,3,5,11,14]. In most cases one simple mode does not reproduce the experimental lineshapes satisfactorily.…”

How can biological modeling help cell biology?

“…9 At a still more fundamental scale, mathematical approaches teach us about inter-and intramolecular events, such as protein-to-protein channeling of cholesterol, 10 metabolite fluxes, 11 or protein side-chain dynamics. 12 While some cell biologists continue to reject the move towards the quantitative/predictive vision, some are ready to "drink the Cool-Aid" and try to embrace the novelty and the promise that computational approaches bring. I belong to the group who think that building the cross-over between experimental and computational analyses will bring us closer to obtaining system-level understanding of cellular life.…”