A spectroscopic assignment technique for membrane proteins reconstituted in magnetically aligned bicelles
- PMID: 22976525
- DOI: 10.1007/s10858-012-9673-y
A spectroscopic assignment technique for membrane proteins reconstituted in magnetically aligned bicelles
Abstract
Oriented-sample NMR (OS-NMR) has emerged as a powerful tool for the structure determination of membrane proteins in their physiological environments. However, the traditional spectroscopic assignment method in OS NMR that uses the "shotgun" approach, though effective, is quite labor- and time-consuming as it is based on the preparation of multiple selectively labeled samples. Here we demonstrate that, by using a combination of the spin exchange under mismatched Hartmann-Hahn conditions and a recent sensitivity-enhancement REP-CP sequence, spectroscopic assignment of solid-state NMR spectra of Pf1 coat protein reconstituted in magnetically aligned bicelles can be significantly improved. This method yields a two-dimensional spin-exchanged version of the SAMPI4 spectrum correlating the (15)N chemical shift and (15)N-(1)H dipolar couplings, as well as spin-correlations between the (i, i ± 1) amide sites. Combining the spin-exchanged SAMPI4 spectrum with the original SAMPI4 experiment makes it possible to establish sequential assignments, and this technique is generally applicable to other uniaxially aligned membrane proteins. Inclusion of an (15)N-(15)N correlation spectrum into the assignment process helps establish correlations between the peaks in crowded or ambiguous spectral regions of the spin-exchanged SAMPI4 experiment. Notably, unlike the traditional method, only a uniformly labeled protein sample is required for spectroscopic assignment with perhaps only a few selectively labeled "seed" spectra. Simulations for the magnetization transfer between the dilute spins under mismatched Hartmann Hahn conditions for various B (1) fields have also been performed. The results adequately describe the optimal conditions for establishing the cross peaks, thus eliminating the need for lengthy experimental optimizations.
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