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001025608 1001_ $$0P:(DE-Juel1)187436$$aStief, Tobias$$b0$$ufzj
001025608 245__ $$aSensitivity-enhanced NMR 15N R1 and R1ρ relaxation experiments for the investigation of intrinsically disordered proteins at high magnetic fields
001025608 260__ $$aOrlando, Fla.$$bAcademic Press$$c2024
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001025608 520__ $$aNMR relaxation experiments provide residue-specific insights into the structural dynamics of proteins. Here, we present an optimized set of sensitivity-enhanced 15N R1 and R1ρ relaxation experiments applicable to fully protonated proteins. The NMR pulse sequences are conceptually similar to the set of TROSY-based sequences and their HSQC counterpart (Lakomek et al., J. Biomol. NMR 2012). Instead of the TROSY read-out scheme, a sensitivity-enhanced HSQC read-out scheme is used, with improved and easier optimized water suppression. The presented pulse sequences are applied on the cytoplasmic domain of the SNARE protein Synpatobrevin-2 (Syb-2), which is intrinsically disordered in its monomeric pre-fusion state. A two-fold increase in the obtained signal-to-noise ratio is observed for this intrinsically disordered protein, therefore offering a four-fold reduction of measurement time compared to the TROSY-detected version. The inter-scan recovery delay can be shortened to two seconds. Pulse sequences were tested at 600 MHz and 1200 MHz 1H Larmor frequency, thus applicable over a wide magnetic field range. A comparison between protonated and deuterated protein samples reveals high agreement, indicating that reliable 15N R1 and R1ρ rate constants can be extracted for fully protonated and deuterated samples. The presented pulse sequences will benefit not only for IDPs but also for an entire range of low and medium-sized proteins.
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001025608 7001_ $$0P:(DE-Juel1)191152$$aVormann, Katharina$$b1$$ufzj
001025608 7001_ $$0P:(DE-Juel1)180657$$aLakomek, Nils-Alexander$$b2$$eCorresponding author$$ufzj
001025608 773__ $$0PERI:(DE-600)1471152-7$$a10.1016/j.ymeth.2024.01.008$$gVol. 223, p. 1 - 15$$p1 - 15$$tMethods$$v223$$x1046-2023$$y2024
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