statut actuel de nus et apsy - bruker · statut actuel de nus et apsy innovation with integrity...
TRANSCRIPT
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Statut actuel de NUS et APSY
Innovation with Integrity
Martial PIOTTO
Bruker BioSpin, France
30ème Réunion Utilisateurs
29 - 30 Novembre 2016
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General features of NUS and APSY
Techniques that allow to collect n-dimensional data without the need for a
complete sampling of the points in the indirect dimension(s)
Non-Uniform Sampling: Technique used for n≥2 (2D/3D/4D) data
Small molecules
Biological applications (Proteins…)
APSY: Technique used for n≥3 (3D…7D...) data
Biological applications (Proteins…)
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Different sampling schemes (3D experiment / Two indirect dimensions)
From: Kazimierczuk, K., V. Orekhov (2015). Non-
uniform sampling: post-Fourier era of NMR data
collection and processing. MRC 53, 921-926
Traditional
sampling
Radial
Sampling
(APSY)
NUS
sampling
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Non Uniform Sampling (NUS)
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Acquisition of NUS data standard since TopSpin 3.0 Non-uniform sampling scheme (3D acquisition with two indirect dimensions)
t 2
t 1 0 10 20 30 40 50 60
0
10
20
30
40
50
complex points
t1
t2
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Data Processing Implementation in Topspin 3.5 pl6
Multidimensional decomposition
MDD-NMR Orekhov et al.
Maximum Entropy (MaxEnt)
Rowland Toolkit Hoch et al.
Forward Maximum Entropy Wagner et al.
Azzara (CCPN) Laue et al.
Multidimensional Fourier Transformation
MFT Kozminski et al.
Marion
Compressed Sensing
CS Orekhov et al.
Kazimierczuk
Nietlispach et al.
…
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Data Analysis Implementation in Topspin 3.5 pl6
Compressed Sensing (Two different algorithms)
CS IST
CS IRLS
With/Without Virtual Echo
2D/3D
Multidimensional decomposition
MDD-NMR (Recursive and non-recursive)
3D/4D
Parallel processing for:
Linux
Mac
Windows
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Full integration of NUS techniques on the spectrometer Acquisition
« eda »
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« edp »
Full integration of NUS techniques on the spectrometer Processing
Available options: MDD
recursive MDD (R-MDD)
CS IST (Iterative Soft Thresholding)
CS IRLS (Iterative Reweighted Least Squares)
All published parameters available through the command line
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NUS Results Increased resolution in f1 for HSQC Ubiquitine 2 mM
Spectral enhancement with Non-Uniform-Sampling
• Ubiquitine 2 mM
• 2D 1H-15N HSQC
• 10 min acquisition time
• CS processing
Traditional sampling
256 points
NUS sampling
1024 points
NUS 25%
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ppm
6.06.57.07.58.08.59.09.5 ppm
9
8
7
6
5
4
3
2
1
0
ppm
6.06.57.07.58.08.59.09.5 ppm
9
8
7
6
5
4
3
2
1
0
Regular sampling
50x100 complex points in 14h37 NUS sampling 20%
50x256 complex points in 7h23
NUS Results Increased resolution for 3D 15N NOESY-HSQC Ubiquitine 2 mM
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ppm
6.06.57.07.58.08.59.09.5 ppm
9
8
7
6
5
4
3
2
1
0
ppm
6.06.57.07.58.08.59.09.5 ppm
9
8
7
6
5
4
3
2
1
0
Regular sampling
50x100 complex points in 14h37 NUS sampling 20%
50x256 complex points in 7h23
NUS Results Increased resolution for 3D 15N NOESY-HSQC Ubiquitine 2 mM
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Regular sampling
50x100 complex points in 14h37
NUS sampling 20%
50x256 complex points in 7h23
NUS Results Increased resolution for 3D 15N NOESY-HSQC Ubiquitine 2 mM
-110 9 8 7 6 5 4 3 2 1 0 ppm
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Practical considerations when acquiring and processing NUS 2D experiments
• For 2D experiments, CS is the method of choice
• The number of complex data points acquired in the indirect dimensions should be similar to the number of expected cross-peaks
• Example: 2D 1H/15N HSQC experiment on ubiquitine:
About 100 cross-peaks expected
128 complex points acquired
• For 2D experiments, NUS is particularly powerful to record 2D HSQC and
HMBC spectra
Typically gain of factor of 2 in acquisition time (50 % NUS)
Higher resolution in the same amount of time
• Use relaxation matched sampling (according to estimated T2)
• Phasing a 2D spectrum processed with NUS
Need to regenerate the imaginary part in f2 with a Hilbert
transform (xht2)
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Practical considerations when acquiring and processing NUS nD experiment (n>2)
• Compressed Sensing (CS) processing can be computer demanding for 3D
spectra (3D NOESY-HSQC experiments)
• MDD processing is less computer demanding (4D data set)
• When possible, limit the processing to the region of interest using STSR and STSI (i.e. for proteins limit the processing to the NH)
• Phasing a nD spectrum processed with NUS
Need to regenerate the imaginary parts with a Hilbert transform
(xht2 ; xht1 …)
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Data Analysis NUS licenses with Topspin 3.5 pl6
NO LICENSES FOR:
Compressed Sensing
CS IST for 2D experiments without virtual echo
SINGLE LICENCE REQUIRED FOR:
Compressed Sensing
CS IST (>2D experiments)
CS IRLS
With/Without Virtual Echo
Multidimensional decomposition
MDD-NMR (Recursive and non-recursive)
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APSY Automated Projection SpectroscopY
Hiller S, Fiorito F, Wüthrich K, Wider G. Automated projection spectroscopy (APSY). PNAS 2005; 102(31): 10876
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- Technique to accelerate the acquisition and analysis of multidimensional data (n≥3)
- Procedure:
Record 2D projections of high-dimensional experiments
Automatic peak picking of the projections
Geometric analysis of the peak list
Generation of a n-dimensional list of cross-peaks (3D 3 frequencies)
Principles of Automated Projection Spectroscopy (APSY)
a=0°
a=90
°
a
E. Kupce and R. Freeman, J. Am. Chem. Soc., 126, 6429, 2004 - Hiller S, and Wider G., Topics in Current Chemistry; 2012
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APSY: General Procedure as implemented in Topspin3.5pl6
- Select experiment
- Generate a list of projection angles
- Acquire corresponding projections as 2D planes
- Process 2D planes
- Analyze 2D planes
- Generate peak list and report
Hiller S, and Wider G., Topics in Current Chemistry; 2012, p. 21-48
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• Uses the acquisition mode (Fn-Type) “PROJECTION SPECTROSCOPY”
• Compatibility of Bruker library pulse programs with APSY mode of
acquisition
No need for specific APSY PP anymore
Standard Pulse Program from the Bruker library can be used
• Full integration in TopSpin (like NUS)
APSY: General Procedure as implemented in Topspin3.5pl6
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APSY in Topspin3.5 pl6
Standard PP
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APSY in Topspin3.5 pl6 apsy command
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APSY in Topspin3.5 pl6 apsy toolbar
• Angles: Create list of projection angles
• GAPRO: Parameters used for peak identification (S/N…)
• Setup: Create all the 2D experiments
• Run: Runs all the different 2D experiments
• Stop: Stop
• Re-Process: Allows to automatically reprocess the entire series of spectra
(Phase…)
• Re-Evaluate: Re-evalute the newly processed data
• Results: Peak list
• Help: Help
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APSY in Topspin 3.5 pl6 Results
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• APSY can be used to record 3D, 4D, 5D and 6D experiments
• Application Intrinsically disordered proteins (IDP)
• NUS processing limited to 2D, 3D and 4D
• NUS processing times can become important for high dimensionality
spectra
APSY: General Features
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APSY: Results 3D + 5D + 5D experiments High precision peak lists
HNCA 3D HACACONH 5D CBCACONH 5D
N CA NH HA CA CO N NH CA CB CO N NH
120.359 54.914 5.914 3.651 43.058 170.957 120.367 5.914 43.073 58.049 170.941 120.354 5.915
103.093 54.480 6.812 3.850 62.387 172.256 103.101 6.813 62.404 77.407 172.274 103.278 6.813
Dutta, S.; Serrano, P.; Proudfoot, A.; Geralt, M.; Pedrini, B.; Herrmann, T.; Wüthrich, K. Journal of Biomolecular NMR 2015, 61, 47-53.
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Cai-1
C‘i-1 Ni-1
Ni
HNi
HNi-1 O
a
b
c
d
e
6D sequential Hi-Ni-COi-1-CAi-1-Ni-1-Hi-1
APSY experiments
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Key Features:
• High Precision: Peak lists of high precision from an optimum number of projections.
APSY experiments
Ubiquitin 2mM
Sequential assignment of [13C,15N]-ubiquitin using the peak list from a 6D-APSY-HNCOCANH experiment.
---8.378123.165754.8708173.6779124.44948.716
-4.178.7145124.36652.7983174.9824122.09998.9591
-1.248.9428122.075158.7179175.6223125.10968.7125
1.4158.7061125.137956.454174.3129133.10588.8578
-4.6358.8405133.013152.3724177.1224102.43328.0613
Hzppmppmppmppmppmppm
(Ni-1-Ni)HiNiCACONi-1Hi-1
---8.378123.165754.8708173.6779124.44948.716
-4.178.7145124.36652.7983174.9824122.09998.9591
-1.248.9428122.075158.7179175.6223125.10968.7125
1.4158.7061125.137956.454174.3129133.10588.8578
-4.6358.8405133.013152.3724177.1224102.43328.0613
Hzppmppmppmppmppmppm
(Ni-1-Ni)HiNiCACONi-1Hi-1
Dutta, S.; Serrano, P.; Proudfoot, A.; Geralt, M.; Pedrini, B.; Herrmann, T.; Wüthrich, K. Journal of Biomolecular NMR 2015, 61, 47-53.
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Acknowledgements
Detlef Moskau
Wolfgang Bermel
W. Mausshardt
E. Schweitzer
V. Orekhov
V. Jaravine
S. Hiller
G. Wider
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