کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10753358 | 1050339 | 2015 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Evaluation of the reliability of the maximum entropy method for reconstructing 3D and 4D NOESY-type NMR spectra of proteins
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کلمات کلیدی
RMSEMaxEntRMSDMDDIRLSNOESYmulti-dimensional decompositionProtein NMR - NMR پروتئینIST - استSpectrum reconstruction - بازسازی طیفIteratively reweighted least-squares - به طور معمول وزن کمترین مربعFourier transform - تبدیل فوریهMaximum Entropy - حداکثر آنتروپیRoot mean square error - ریشه میانگین خطای مربعCompressed sensing - سنجش فشرده root mean square deviation - میانگین انحراف مربع ریشه
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
زیست شیمی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
We compared MaxEnt with other methods in reconstructing 3D NOESY data acquired with variously reduced sparse sampling schedules and found that MaxEnt is robust, quick and competitive with other methods. Next, nonlinear sampling and MaxEnt processing were applied to 4D NOESY experiments, and the effect of the artefacts of MaxEnt was evaluated by calculating 3D structures from the NOE-derived distance restraints. Our results demonstrated that sufficiently converged and accurate structures (RMSD of 0.91Â Ã
to the mean and 1.36Â Ã
to the reference structures) were obtained even with NOESY spectra reconstructed from 1.6% randomly selected sampling points for indirect dimensions. This suggests that 3D MaxEnt processing in combination with nonlinear sampling schedules is still a useful and advantageous option for rapid acquisition of high-resolution 4D NOESY spectra of proteins.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biochemical and Biophysical Research Communications - Volume 457, Issue 2, 6 February 2015, Pages 200-205
Journal: Biochemical and Biophysical Research Communications - Volume 457, Issue 2, 6 February 2015, Pages 200-205
نویسندگان
Yoshiki Shigemitsu, Teppei Ikeya, Akihiro Yamamoto, Yuusuke Tsuchie, Masaki Mishima, Brian O. Smith, Peter Güntert, Yutaka Ito,