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HIFI NMR : part1 automated backbone assignments using 3D->2DPowerPoint Presentation

HIFI NMR : part1 automated backbone assignments using 3D->2D

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HIFI NMR : part1automated backbone assignments using 3D->2D

Marco Tonelli

National Magnetic Resonance Facility At Madison NMRFAM

t1

t

t1

t2

t2

t3

t1x t2: 128x128=16,384 FIDs

5 days 16 hrs.

1 FID

30 sec.

t1: 128 FIDs

64 min.

1D

2D

3D

t1x t2 x t3 : 32x32x32=32,768 FIDs

11days 9 hrs.

4D

Recording multidimensional experiments is time costly

In conventional multidimensional experiments, all the individual frequency domains are incremented (sampled) independently

- Increasing number of indirect dimensions in conventional multidimensional:
- collection time increases exponentially
- need to reduce number of increments to keep collection time reasonable: low resolution
- not very practical above 3 dimensions
- impossible above 4 dimensions

Reduced Sampling

Reduced Dimensionality

Hadamard spectroscopy

single-scan NMR

so-fast NMR

RD spectrum

t1/t2

t3

t1

t2

t1 and t2 are incremented simultaneously

point by point

t3

t1 and t2 are incremented independently

Reduced Dimensionality Techniques

two or more indirect dimensions are evolved simultaneously

t3

t1

t2

preparation

mix/prep

mixing

Reduced Dimensionality Techniques …

F1

F2

t3

t1

t2

preparation

mix/prep

mixing

F1 = x,y

F2 = y

F1 = x,y

F2 = x

t1/t2

t1/t2

RD spectrum

RD spectrum

F1 = x,y

sinwt1 x sinwt2

sinwt1 x coswt2

coswt1 x sinwt2

coswt1 x coswt2

t1

2D spectrum

t3

t3

FT

FT

sinwt1

coswt1

d1-d2

d1- d2

t3

w1/w2

FT

w1/w2

d1+d2

d1+d2

w3

w3

d1

w1

d1-d2

w3

w1/w2

d1+d2

w3

w3

Reduced Dimensionality Techniques …

GFT

TPPI Method

d1+ d2

+ d2

d1+ d2

2D

w1/w2

TPPI offset

w1

d1

- d2

d1- d2

d1- d2

w3

w3

The peaks in RD experiments can either be separated into different spectra (GFT - Szyperski) or into different regions of the same spectrum (TPPI - Gronenborn)

1H-13C plane

of CBCA(CO)NH

13C

0° plane

1H

90°

15N

13C

1H-15N plane

of CBCA(CO)NH

15N

90° plane

15N

1H

13C

1H

1H

q

15N

45°

1H-13C/15N plane

of CBCA(CO)NH

13C/15N

tilted plane

13C

1H

1H

2D RD planes of 3D spectra

2D projections of 3D spectra tilted planes

Reduced Dimensionality Techniques …

t

13C

15N

t

15N

13C

1H

By changing the ratio between the two simultaneously evolving dimensions we can change the projection angle of the tilted plane

q

20°

70°

45°

q

2D planes of 3D CBCA(CO)NH

Reduced Dimensionality Techniques …

3D

reconstruction

3D object

reconstructed

object

15N

13C

90

1H

3D reconstrucion

0

q

Projection Reconstruction

Simple 3D objects can be reconstructed from 2D projections collected at different angles

In principle, it is feasible to reconstruct a 3D spectrum from a number of 2D tilted planes collected at different angles.

Reduced Dimensionality Techniques …

n-dimensional experiments are run as two-dimensional RD spectra

multiple tilted planes are collected at different angles

n-dimensional spectra are reconstructed from several tilted planes

split peaks can be separated into different spectra (GFT) or different regions of the same spectrum (TPPI)

indirect frequencies are extracted from analysis of split patterns

GFT / TPPI method

Projection-Reconstruction

High-resolution Iterative Frequency Identification

HIFI

simultaneously evolving indirect frequencies are extracted from two-dimensional RD spectra

multiple tilted planes are used

angle of tilted planes is chosen adaptively in real time

Reduced Dimensionality Techniques …

it relies on combining multiple indirect dimension to resolve overlapped peaks

multidimensional frequency information can only be extracted after spectra reconstruction

- with each added indirect dimension:
- S/N is reduced by √2
- collection time is doubled

reconstructing spectra can be very complicated or impossible with overlapped peaks and/or low S/N

analysis can be very complicated

difficult to automate

difficult to automate

by changing the tilt angle, HIFI has greater potential of resolving overlapped peaks than GFT/TPPI methods

since only frequency information is extracted from tilted planes, HIFI does not need to resolve the problem of reconstructing nD volumes

easier to analyze

easier to automate

by adaptively choosing the next tilt angle, HIFI optimizes information gain while minimizing time collection

GFT / TPPI method

Projection-Reconstruction

HIFI

tilted angle

does

tilted plane add

new information

???

YES

this process can be automated in vnmr

NO

HIFI flowchart

mORTHO0 macro

orthogonal planes

hifi.sh

tilted plane

generate_peaklist.sh

peak list

predicted chemical shift distribution

orthogonal planes

assign a probability of a peak being in a given voxel, p

0°

90°

dispersion function, fq(p),measures the dispersion of the putative peaks on the selected tilted plane

find a tilt angle that maximizes a dispersion function

fq (p)

collect tilted plane

does

tilted plane add

new information

???

X°

NO

YES

peak list

HIFI algorithm for predicting best tilted angle

Eghbalnia et al JACS 127 (36), 12528 -12536, 2005

putative peaks from C-H/N-H planes

add 45° tilted plane

add additional tilted planes

HIFI on CBCA(CO)NH

Combined peaks from HIFI planes are in magenta

Hand picked peaks from 3D spectrum in green

Modified BioPack experiments for backbone assignments

HNCO

NH sensitivity enhanced

TROSY option

HN(CO)CA

HNCA

CBCA(CO)NH

HN(CA)CB

HNCACB

standard sequences are robust and offer the best S/N for backbone assignments

we rely on HIFI ability to adaptively select tilted planes to resolve overlapped peaks

- added HIFI option for collecting tilted planes
- 13C and 15N indirect dimensions are evolved simultaneously

- added semi-constant time 15N evolution to allow collecting tilted planes with more indirect points for higher resolution
- optimized for cryogenic probes

Using HIFI for backbone assignments

Needs AUTOMATION !!!

outline of vnmr macro for automated HIFI data collection

preparation - orthogonal planes

- run othogonal planes for all experiments

- all experiments list:
- collect 0º plane
- adjust 13C s.w.

- 1H-15N HSQC :
- use as 90º plane
- adjust 15N s.w.

input list of experiments

number of residues = XX

HNCO

HN(CO)CA

HNCA

CBCA(CO)NH

HNCACB

HN(CA)CB

hnco_tilt_0

hncoca_tilt_0

hnca_tilt_0

cbcaconh_tilt_0

hncacb_tilt_0

hncb_tilt_0

hnco_hsqc

hnco_hsqc

hnco_hsqc

hnco_hsqc

hnco_hsqc

hnco_hsqc

- save orthogonal planes

- process orthogonal planes
- optimize processing parameters

experiment #1 in list

run HIFI macro - tilted planes

run tilt angle predicting program

process tilted plane

save tilted plane

read predicted

tilt angle

tilt>0

run experiment to collect tilted plane at suggested angle

next experiment in list

tilt=0

run program to extract 3D frequencies

3D peak list

run probabilistic

backbone assignments

all

experiments

in list completed

??

NO

YES

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

90º

63º

62º

64º

51º

47º

43º

41º

42º

38º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

0º

34º

20º

2

2

4

3

77º

69º

70º

69º

45º

52º

53º

44º

34º

26º

15º

2

2

3

4

71º

71º

63º

59º

67º

52º

41º

51º

46º

56º

39º

38º

33º

40º

34º

27º

29º

22º

18º

7

4

4

4

70º

63º

77º

60º

57º

65º

49º

53º

48º

46º

54º

44º

40º

35º

42º

32º

26º

31º

18º

4

4

7

4

70º

59º

60º

62º

53º

42º

53º

41º

35º

31º

41º

28º

28º

14º

11º

4

4

7

57º

45º

42º

39º

45º

31º

39º

33º

40º

35º

31º

24º

37º

24º

29º

12º

18º

26º

18º

14º

15º

5º

5

5

11º

8º

6

9

3º

~9hrs

~48hrs

~12hrs

~14hrs

brazzein - 53 a.a.

ubiquitin

76 a.a.

flavodoxin

176 a.a.

wild-type

RI mutant

HNCO

HN(CO)CA

HNCA

CBCA(CO)NH

HN(CA)CB

HNCACB

HIFI backbone assignments - recap

- we have developed HIFI for extracting 3D peaks using 2D tilted planes
- by adaptively predicting the best tilt angles, we guarantee that all available 3D data is extracted using the minimum number of tilted planes
- we have adapted the most robust 3D experiments for backbone assignments
- to be recorded using HIFI-NMR
- we have successfully automated HIFI backbone data collection for proteins of small/medium size
- automated HIFI is robust and allows to extract 3D peaks lists with:
- least amount of spectrometer time
- minimum human intervention

longer time≡higher S/N

longer time≡higher S/N

resolve overlap

resolve overlap

complicated

complicated

lower S/N

HIFI

4

3

TROSY

6

5

4

conventional NMR experiments

3

2

protein sample

3

size

Total # dim

Acquired # dim

Acquired # dim

where is HIFI going …

deuteration

selective labeling

peak overlap

T2 relaxation

Improving algorithm for peak detection

Improving algorithm for tilt angle prediction

Combine information from different experiments

HIFI NMR : part2conclusions and other applications

Marco Tonelli

National Magnetic Resonance Facility At Madison NMRFAM

collect all the information needed

HIFI

adaptive

efficient :

collect only the minimum amount information needed to answer the question

HIFI

orthogonal

plane

predict best

angle

collect

tilted plane

peak list

HIFI

BACKBONE ASSIGNMENTS

orthogonal

plane

predict best

angle

collect

tilted plane

HIFI: application to chemical shift assignments

TODAY:

HNCO

HN(CO)CA

HNCA

CBCA(CO)NH

HNCACB

HN(CA)CB

collect all the information needed

HIFI

adaptive

efficient :

collect only the minimum amount information needed to answer the question

orthogonal

plane

predict best

angle

collect

tilted plane

peak list

BACKBONE ASSIGNMENTS

HIFI

orthogonal

plane

predict best

angle

collect

tilted plane

peak list

HIFI: application to chemical shift assignments

TOMORROW:

HNCO

HN(CO)CA

HNCA

CBCA(CO)NH

HNCACB

HN(CA)CB

collect all the information needed

HIFI

adaptive

efficient :

collect the only the minimum amount information needed to answer the question

HIFI

collect preliminary data

select experiment

predict best tilt

collect

tilted plane

BACKBONE

ASSIGNMENTS

HIFI: application to chemical shift assignments

THE DAY AFTER TOMORROW:

pool of

experiments

tilted plane

t1/t2

t3

t1

t2

t3

HIFI: other applications

any application that makes use of information extracted from a 3D experiment can be speeded up by recording a tilted plane instead

HIFI can then be used to ensure that maximum information is recovered by predicting the best angle to use for recording the tilted plane

13C + 15N

1H

13C - 15N

-q

1H

15N

13C

1H

HIFI: other applications

- two tilted planes are obtained for each experiment run: “plus” and “minus”
- different peak distribution – bigger potential of resolving overlapped peaks
- different noise distribution – can provide measure of confidence of data obtained by analyzing spectra

reference

15N

15N

13C

13C

1H

1H

HIFI: extraction of RDC

Example:

extraction of RDCC’N using a modified HNCO pulse sequence (Bax)

Conventional method:

- record two 3D C’-N-H experiments:
- reference spectrum
- attenuated spectrum - intensity of peaks is modulated by coupling: JC’N + DC’N

- extract JC’N + DC’N coupling from the ratio between intensity of corresponding peaks in the reference and attenuated spectra

- repeat for isotropic and aligned samples

reference

attenuated

reference

attenuated

-q

+q

13C + 15N

1H

1H

-q

13C - 15N

1H

1H

HIFI: extraction of RDC

HIFI method:

- record two tilted C’-N-H planes at the optimal tilt angle:
- reference spectrum
- attenuated spectrum - intensity of peaks is modulated by coupling: JC’N + DC’N

- extract JC’N + DC’N coupling from the ratio between intensity of corresponding peaks in the reference and attenuated spectra

- analyze “plus” and “minus” planes independently
- compare the results from the two plenes to get measure of data confidence
- combine the results from the two planes

- repeat for isotropic and aligned samples

tilt prediction and peak extraction algorithms

Arash Barhami

Hamid Eghbalnia

pulse sequences

vnmr automation

RDC extraction

Marco Tonelli

Klaas Hallenga

Gabriel Cornilescu

sample preparation

Fariba Assadi-Porter

Shanteri Shingh

Rob Tyler

Anna Fuzery

Nick Reiter

Claudia Cornilescu

Who did the work ?

John Markley

Milo Westler

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