کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5406716 | 1393185 | 2010 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fast acquisition of high resolution 4-D amide-amide NOESY with diagonal suppression, sparse sampling and FFT-CLEAN
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موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی تئوریک و عملی
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چکیده انگلیسی
Amide-amide NOESY provides important distance constraints for calculating global folds of large proteins, especially integral membrane proteins with β-barrel folds. Here, we describe a diagonal-suppressed 4-D NH-NH TROSY-NOESY-TROSY (ds-TNT) experiment for NMR studies of large proteins. The ds-TNT experiment employs a spin state selective transfer scheme that suppresses diagonal signals while providing TROSY optimization in all four dimensions. Active suppression of the strong diagonal peaks greatly reduces the dynamic range of observable signals, making this experiment particularly suitable for use with sparse sampling techniques. To demonstrate the utility of this method, we collected a high resolution 4-D ds-TNT spectrum of a 23 kDa protein using randomized concentric shell sampling (RCSS), and we used FFT-CLEAN processing for further reduction of aliasing artifacts - the first application of these techniques to a NOESY experiment. A comparison of peak parameters in the high resolution 4-D dataset with those from a conventionally-sampled 3-D control spectrum shows an accurate reproduction of NOE crosspeaks in addition to a significant reduction in resonance overlap, which largely eliminates assignment ambiguity. Likewise, a comparison of 4-D peak intensities and volumes before and after application of the CLEAN procedure demonstrates that the reduction of aliasing artifacts by CLEAN does not systematically distort NMR signals.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Magnetic Resonance - Volume 204, Issue 1, May 2010, Pages 173-178
Journal: Journal of Magnetic Resonance - Volume 204, Issue 1, May 2010, Pages 173-178
نویسندگان
Jon W. Werner-Allen, Brian E. Coggins, Pei Zhou,