Statut actuel de NUS et APSY

Innovation with Integrity

Martial PIOTTO

Bruker BioSpin, France

30ème Réunion Utilisateurs

29 - 30 Novembre 2016

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…)

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

Non Uniform Sampling (NUS)

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

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.

…

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

Full integration of NUS techniques on the spectrometer Acquisition

« eda »

« 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

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%

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

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

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

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)

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 …)

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)

APSY Automated Projection SpectroscopY

Hiller S, Fiorito F, Wüthrich K, Wider G. Automated projection spectroscopy (APSY). PNAS 2005; 102(31): 10876

- 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

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

• 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

APSY in Topspin3.5 pl6

Standard PP

APSY in Topspin3.5 pl6 apsy command

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

APSY in Topspin 3.5 pl6 Results

• 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

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.

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

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.

Acknowledgements

Detlef Moskau

Wolfgang Bermel

W. Mausshardt

E. Schweitzer

V. Orekhov

V. Jaravine

S. Hiller

G. Wider

www.bruker.com