National Magnetic Resonance Facility At Madison NMRFAM HIFI NMR : part1 automated backbone assignments using 3D->2D Marco Tonelli National Magnetic Resonance Facility At Madison NMRFAM

Recording multidimensional experiments is time costly In conventional multidimensional experiments, all the individual frequency domains are incremented (sampled) independently t1 t2 t3 t1x t2: 128x128=16,384 FIDs 5 days 16 hrs. 3D t2 t1 t1: 128 FIDs 64 min. 2D t 1 FID 30 sec. 1D 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 t1x t2 x t3 : 32x32x32=32,768 FIDs 11days 9 hrs. 4D

Reduced Dimensionality Fast methods Reduced Sampling Reduced Dimensionality Hadamard spectroscopy single-scan NMR so-fast NMR

Reduced Dimensionality Techniques two or more indirect dimensions are evolved simultaneously t3 t1 t2 preparation mix/prep mixing t2 t3 t1 Conventional 3D spectrum t1 and t2 are incremented independently t3 t1/t2 t1 and t2 are incremented simultaneously point by point RD spectrum

d1- d2 d1- d2 d1+ d2 d1+ d2 d1 d1- d2 d1+ d2 t2 t3 t1 w1/w2 w3 FT Reduced Dimensionality Techniques … mix/prep mixing t2 t3 preparation t1 F1 F2 d1+ d2 d1- d2 w1/w2 w3 t3 coswt1 x coswt2 sinwt1 x coswt2 RD spectrum t1/t2 FT F1 = x,y F2 = x d1+ d2 d1- d2 w1/w2 w3 t3 coswt1 x sinwt2 sinwt1 x sinwt2 RD spectrum t1/t2 FT F1 = x,y F2 = y t3 t1 coswt1 sinwt1 2D spectrum FT d1 w1 w3 F1 = x,y d1- d2 w1/w2 w3 d1+ d2

Reduced Dimensionality Techniques … The peaks in RD experiments can either be separated into different spectra (GFT - Szyperski) or into different regions of the same spectrum (TPPI - Gronenborn) w1/w2 GFT d1+ d2 d1- d2 w3 - d2 + d2 TPPI Method d1+ d2 d1- d2 w3 TPPI offset 2D d1 w1

2D projections of 3D spectra  tilted planes 2D RD planes of 3D spectra 2D projections of 3D spectra  tilted planes 1H 13C 15N 0° plane 1H-13C plane of CBCA(CO)NH 0° 1H 13C 15N 90° plane 1H-15N plane of CBCA(CO)NH 90° 1H 13C 15N q tilted plane 1H-13C/15N plane of CBCA(CO)NH 45° 13C/15N

q t Reduced Dimensionality Techniques … By changing the ratio between the two simultaneously evolving dimensions we can change the projection angle of the tilted plane t 15N 13C q 20° 45° 70° 1H 13C 15N q 2D planes of 3D CBCA(CO)NH

Projection Reconstruction Reduced Dimensionality Techniques … Projection Reconstruction Simple 3D objects can be reconstructed from 2D projections collected at different angles reconstructed object 3D reconstruction 3D object q  90 0 1H 13C 15N 3D reconstrucion In principle, it is feasible to reconstruct a 3D spectrum from a number of 2D tilted planes collected at different angles.

Projection-Reconstruction Reduced Dimensionality Techniques … GFT / TPPI method Projection-Reconstruction n-dimensional experiments are run as two-dimensional RD spectra multiple tilted planes are collected at different angles split peaks can be separated into different spectra (GFT) or different regions of the same spectrum (TPPI) n-dimensional spectra are reconstructed from several tilted planes indirect frequencies are extracted from analysis of split patterns 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

Projection-Reconstruction Reduced Dimensionality Techniques … GFT / TPPI method Projection-Reconstruction 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 HIFI 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

HIFI flowchart this process can be automated in vnmr mORTHO0 macro orthogonal planes predict next best tilted angle does tilted plane add new information ??? this process can be automated in vnmr YES hifi.sh tilted plane NO generate_peaklist.sh peak list

predicted chemical shift distribution HIFI algorithm for predicting best tilted angle orthogonal planes 0° 90° predicted chemical shift distribution assign a probability of a peak being in a given voxel, p YES collect tilted plane X° find a tilt angle that maximizes a dispersion function fq (p) dispersion function, fq (p), measures the dispersion of the putative peaks on the selected tilted plane does tilted plane add new information ??? NO peak list 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

Needs AUTOMATION !!! Using HIFI for backbone assignments HNCO HN(CO)CA HNCA CBCA(CO)NH HN(CA)CB HNCACB Modified BioPack experiments for backbone assignments NH sensitivity enhanced TROSY option 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 Needs AUTOMATION !!!

outline of vnmr macro for automated HIFI data collection 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 input list of experiments preparation - orthogonal planes run othogonal planes for all experiments process orthogonal planes optimize processing parameters save orthogonal planes all experiments list: collect 0º plane adjust 13C s.w. 1H-15N HSQC : use as 90º plane adjust 15N s.w. run tilt angle predicting program read predicted tilt angle experiment #1 in list run HIFI macro - tilted planes process tilted plane save tilted plane run experiment to collect tilted plane at suggested angle tilt>0 NO next experiment in list tilt=0 run program to extract 3D frequencies all experiments in list completed ?? 3D peak list YES run probabilistic backbone assignments

~9hrs ~12hrs ~14hrs ~48hrs brazzein - 53 a.a. ubiquitin 76 a.a. flavodoxin 176 a.a. wild-type RI mutant 0º 90º 47º 62º 2 52º 69º 51º 71º 29º 38º 4 49º 63º 44º 32º 42º 59º 31º 28º 39º 45º 18º 15º 5 0º 90º 43º 64º 2 45º 77º 41º 63º 27º 33º 4 65º 54º 42º 31º 24º 12º 5º 5 0º 90º 41º 38º 34º 3 15º 69º 44º 56º 67º 22º 40º 4 48º 57º 31º 53º 62º 11º 45º 37º 26º 8º 6 71º 0º 90º 42º 51º 20º 63º 4 26º 70º 34º 53º 46º 59º 18º 39º 52º 7 60º 35º 24º 14º 11º 33º 3º 29º 9 28º 41º HNCO HN(CO)CA HNCA CBCA(CO)NH HN(CA)CB HNCACB ~9hrs ~12hrs ~14hrs ~48hrs

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

where is HIFI going … HIFI TROSY 4 6 3 5 4 3 size 2 3 deuteration longer time ≡ higher S/N resolve overlap 4 complicated 3 longer time ≡ higher S/N resolve overlap complicated lower S/N 6 5 4 deuteration selective labeling protein sample conventional NMR experiments HIFI size peak overlap T2 relaxation TROSY Improving algorithm for peak detection 3 Acquired # dim Improving algorithm for tilt angle prediction Acquired # dim 2 Combine information from different experiments Total # dim 3

The BIG picture NMR data collection structure calculation chemical shift assignments structure calculation

National Magnetic Resonance Facility At Madison NMRFAM HIFI NMR : part2 conclusions and other applications Marco Tonelli National Magnetic Resonance Facility At Madison NMRFAM

TODAY: HIFI: application to chemical shift assignments HIFI robust : collect all the information needed HIFI adaptive efficient : collect only the minimum amount information needed to answer the question TODAY: BACKBONE ASSIGNMENTS HNCO HN(CO)CA HNCA CBCA(CO)NH HNCACB HN(CA)CB collect tilted plane predict best angle orthogonal plane HIFI collect tilted plane predict best angle orthogonal plane HIFI peak list

TOMORROW: HIFI: application to chemical shift assignments HIFI robust : collect all the information needed HIFI adaptive efficient : collect only the minimum amount information needed to answer the question TOMORROW: BACKBONE ASSIGNMENTS HNCO HN(CO)CA HNCA CBCA(CO)NH HNCACB HN(CA)CB HIFI peak list collect tilted plane predict best angle orthogonal plane collect tilted plane predict best angle orthogonal plane peak list

collect preliminary data HIFI: application to chemical shift assignments robust : collect all the information needed HIFI adaptive efficient : collect the only the minimum amount information needed to answer the question THE DAY AFTER TOMORROW: HIFI collect preliminary data BACKBONE ASSIGNMENTS pool of experiments select experiment predict best tilt collect tilted plane

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 t2 t3 t1 Conventional 3D spectrum t1/t2 tilted plane HIFI can then be used to ensure that maximum information is recovered by predicting the best angle to use for recording the tilted plane

HIFI: other applications +q -q 13C + 15N 13C - 15N 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

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 1H 13C 15N reference attenuated 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

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 +q 13C + 15N 1H reference attenuated -q 1H 13C - 15N reference attenuated 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 Who did the work ? pulse sequences vnmr automation RDC extraction Marco Tonelli Klaas Hallenga Gabriel Cornilescu tilt prediction and peak extraction algorithms Arash Barhami Hamid Eghbalnia Fariba Assadi-Porter Shanteri Shingh Rob Tyler Anna Fuzery Nick Reiter Claudia Cornilescu sample preparation John Markley Milo Westler

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