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Judith Klein-Seetharaman Department of Structural Biology

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1 Judith Klein-Seetharaman Department of Structural Biology
NMR Spectroscopy Judith Klein-Seetharaman Department of Structural Biology

2 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

3 Resources Websites http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

4 Resources Books NMR Books:
Protein NMR Techniques (Methods in Molecular Biology) by A. Kristina Downing (Editor) Protein NMR Spectroscopy: Principles and Practice by John Cavanagh, Wayne J. Fairbrother, III, Arthur G. Palmer, Nicholas J. Skelton, Mark Rance Spin Dynamics: Basics of Nuclear Magnetic Resonance by Malcolm H. Levitt Principles of Nuclear Magnetic Resonance in One and Two Dimensions by Richard R. Ernst, Geoffrey Bodenhausen, Alexander Wokaun 200 and More NMR Experiments: A Practical Course by Stefan Berger, Siegmar Braun Basic One- and Two-Dimensional NMR Spectroscopy by Horst Friebolin NMR Spectroscopy: Basic Principles, Concepts, and Applications in Chemistry by Harald Günther NMR Data Processing by Hoch NMR: The Toolkit by P. J. Hore, J. A. Jones, S. Wimperis Nuclear Magnetic Resonance by P. J. Hore NMR for Physical and Biological Scientists by Thoma Pochapsky Understanding NMR Spectroscopy by James Keeler NMR of Proteins (Topics on Molecular and Structural Biology) by G. M. Clore, A. M. Gronenborn The Nuclear Overhauser Effect in Structural and Conformational Analysis by David Neuhaus, Michael P. Williamson Biophysics Books with chapters on NMR: Biophysical Chemistry: Part II: Techniques for the Study of Biological Structure and Function by Charles R Cantor, Paul R Schimmel Principles of Physical Biochemistry by Kensal E van Holde, Curtis Johnson, Pui Shing Ho 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

5 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

6 Nuclei in a magnetic field
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

7 Energy Difference http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

8 Macroscopic View http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

9 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

10 Experiment: Recycle delay dependent on T1 relaxation
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

11 The NMR signal Analogy: conducting loop rotating in a magnetic field:
Analogy: conducting loop rotating in a magnetic field: 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

12 Fourier Transform http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

13 Soft pulses vs. hard pulses
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

14 Obtaining a spectrum http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

15 Product Operator Formalism
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

16 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

17 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

18 HSQC Experiment http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

19 HSQC TOCXY http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf 9/22/2018
Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

20 Signal Intensity Boltzmann distribution
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

21 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

22 Sample requirements: Sources
Think of the requirements that we may need to fulfil! 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

23 Comparison of expression systems for rhodopsin
Example Comparison of expression systems for rhodopsin spacebio.net/modules/ mb_teare.html genetics.med.harvard.edu/ ~winston/ ___________ ___________ ___________ ___________ ___________ ___________ What are the advantages and disadvantages of each expression system? 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

24 Where can we get these molecules from?
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

25 Sources of biomolecules
Summary Native sources Best quality (correct fold, posttranslational modifications etc.) Not always best quantity Limitations in labeling No mutants Chemical synthesis Good for small molecules Not good for large proteins Biosynthesis A variety of expression systems exist, all with their advantages and disadvantages. Required for isotope labeling for NMR 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

26 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

27 NMR parameters Chemical Shift See handout
See handout 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

28 Chemical shift perturbation
Figure 2 in “Cap-free structure of eIF4E suggests a basis for conformational regulation by its ligands Laurent Volpon, Michael J Osborne, Ivan Topisirovic, Nadeem Siddiqui and Katherine LB Borden The EMBO Journal (2006) 25, 5138–5149 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

29 The Nuclear Overhauser Effect
NMR parameters The Nuclear Overhauser Effect 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

30 Measuring NOE’s http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

31 NMR Parameters Dipolar Couplings
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

32 NMR Structure of Bcl-XL Bound to BH3 Peptide
Structure was solved with a homolog of BH3 helix. Protein-protein interaction groove was identified on anti-apoptotic Bcl-XL. PDB ID:1G5J Drug design approach: SAR by NMR Identify a drug that binds in the BH3 pocket of Bcl-XL, inhibit binding to BID -> normal apoptosis. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

33 NMR parameters chemical shifts NOE Dipolar coupling coupling constants
HetNOE longitudinal relaxation rates (R1) transverse relaxation rates (R2) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

34 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

35 Theory -NMR Relaxation mechanism
NMR Dynamics on Different Time Scales time scale type ns-ps fast internal motions us-ms slow internal motions ms-days proton exchange Proteins are dynamic molecules, they often undergo conformational changes while performing thir specific functions. NMR can be used to monitor the dynamic behavior on a broad range of timescales. Typically, 3 types, fast internal motions, slow internal motions and proton exchange. These motions are reported by measurement parameters. • rotational diffusion (τc) • translational diffusion (D) • internal dynamics of backbone and sidechains (τi) • degree of order for backbone and sidechains (S2) • conformational exchange (Rex) • interactions with other molecules (kon,koff) Protein are dynamic molecules NMR dynamics can be used on a broad range of timescales. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

36 Relaxation Longitudinal relaxation (T1): return of longitudinal (z-component) to its equilibrium value Transverse relaxation (T2): decay of transverse (x,y-component) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

37 T1 Relaxation http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

38 Experiment: Recycle delay dependent on T1 relaxation
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

39 T2 Relaxation http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

40 Mechanisms of Relaxation
Dipolar interaction 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

41 Mechanisms of Relaxation
Chemical shift anisotropy Scalar relaxation (chemical exchange, rapid T1 relaxation) Quadrupolar relaxation Spin rotation relaxation Interaction with unpaired electrons 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

42 Quantification of motion - strategies to obtain dynamic information from NMR relaxation experiment
Measure R1, R2, heteronuclear NOE “model free” approach Get order parameter S2 ,τe, τm This is the strategies to get dynamic information from experiment. First, then… 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

43 Lipari-Szabo Model Free Approach
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

44 Lipari Szabo Order parameters S2
Order parameters S2 τe, effective correlation function time for internal motions τm, overall tumbling correlation time for global motions 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

45 Lipari–Szabo “model-free” approach - don’t depend on a specific physical model
Estimate (τm) from R2/R1 for a selected subset of the residues fits to the observed relaxation data using various regression variables model-selection criteria are used to decide which choice is appropriate for each residue Reoptimize using the selected models. Uncertainties in the optimized parameters were obtained by Monte Carlo simulation. Model free approach is the most popular analytical methods, the name “model free” means it doesn’t depend on a specific physical model. Assuming that the global and internal motions are separable. It is a good approximation if the internal motions are sufficiently small. J(w); spectral density, is the fourier transform of the correlation function. (the function of correlation time) is a measure of amount of the motion present at different frequencies, ω, to cause the transitions (relaxation) S2 is the order parameter for the slow motion, Sf2 is the order parameter for the fast motion, and te is the effective correlation time for the slow motion, t-1 = te-1 + tm -1. Michael Andrec, Gaetano T. Montelione, Ronald M. Levy Journal of Magnetic Resonance 139, 408–421 (1999) “Model-free” is the most popular method to calculate dynamic parameters 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

46 Inversion Recovery: Measure T1
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

47 Carr-Purcell spin echo: Measure T2
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

48 Comparison of T1 and T2 relaxation
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

49 Dependence of T1, T2 on Tumbling Time
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

50 Chemical Exchange http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

51 Chemical Exchange http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

52 Example: GCN4-58 complexed with DNA -dynamics of complex formation
dynamics of the basic leucine zipper domain of the dimeric yeast transcription factor GCN4 (GCN4-58) as it relates to DNA binding Ribbon diagram of GCN4-58 complexed with DNA. The section in blue is the binding domain. The section in red is the leucin zipper dimerization domain. The thicker and deeper purple regions indication the highest degree of disorder. low S2 indicate high flexibility. S2 can be used to estimate energetics. John Cavanagh and Mikael Akke nature structural biology • volume 7 number 1 • january 2000 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

53 Dynamics in folded/unfolded lysozyme
Arrows indicate oxidized (all disulfide bonds present) lysozyme Folded: 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

54 Using NMR to identify residual structure
Can in principle use all parameters: chemical shifts coupling constants HetNOE longitudinal relaxation rates (R1) transverse relaxation rates (R2) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

55 Chemical shift differences between unfolded lysozyme and random coil
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

56 Relaxation Rates in Unfolded Lysozyme
Unfolded lysozyme can be studied in 8 M urea. Unfolded lysozyme can also be studied without urea, if the disulfide bonds are reduced and the cysteines are derivatized to prevent them from forming disulfide bonds. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

57 Relaxation Rates in Unfolded Lysozyme
What do you observe? 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

58 Relaxation Rates in Unfolded Lysozyme
Regions with higher relaxation rates are localized as clusters.  Presence of clusters of residual structure that are restricted in conformational space, thus relax faster. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

59 Analysis of the relaxation data
Three means of analysis have been proposed: Model-free approach Cole-Cole distributions Gaussian clusters However: What gives rise to these clusters is not known. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

60 Relaxation Rates in Unfolded Lysozyme
å = - 1 | int ) ( N j i rinsic e R l Random Coil Model of Segmental Motion å - + 2 | x b i Ae + Gaussian Distributions of Deviations 1. 2. 3. 4. 5. 6. There are six clusters of residual structure in HEWL-SME. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

61 Mapping of residual structure on the native structure
How would you test what stabilizes these clusters? 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

62 Hydrophobic clusters of residual structure
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

63 How would you test for the presence of long-range interactions
How would you test for the presence of long-range interactions? Approach: Study effect of mutation 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

64 Effect of mutation on chemical shifts
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

65 Effect of mutation on relaxation rates
A single point mutation, W62G in cluster 3, disrupts all clusters in reduced and methylated lysozyme. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

66 Effect of mutation on chemical shifts
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

67 Effect of mutation on relaxation rates
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

68 Model for unfolded ensemble
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

69 Compactness by NMR 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

70 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

71 NMR spectroscopy Size Stability Sample homogeneity Need for labeling
General limitations Size Stability Sample homogeneity Need for labeling Quantities and source of biomolecules 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

72 Unfolded proteins have a small chemical shift dispersion.
Resolution Wide versus small chemical shift dispersion folded unfolded Unfolded proteins have a small chemical shift dispersion. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

73 1d 1H NMR spectra 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

74 HSQC spectra 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

75 Example Solution NMR of DAGK
1H,15N-HSQC spectrum of a 120 aa long membrane protein in DPC micelles Diacylglycerol kinase: Charles R. Sanders, Frank Sonnichsen (2006) Solution NMR of membrane proteins: practice and challenges. Magn. Reson. Chem. 2006; 44: S24–S40 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

76 Example Solution NMR of Rhodopsin
It’s a headache. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

77 How can you test your hypothesis?
What is signal 1? How can you test your hypothesis? 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

78 Assignment of Signal 1 NMR Spectroscopy
Black: original spectrum, red: C-terminus, green: N-terminus (after AspN cleavage) An enzyme was used to cleave off the C-terminus at the site indicated below: 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

79 Traditional Solution NMR Approaches
Problems with full-length membrane proteins in detergents Size – is not the only problem (Trosy does not work for helical membrane proteins) Conformational exchange – fluctuations in the detergent micelle environment lead to fast relaxation thus signal decay Spin diffusion – cannot deuterate samples from mammalian cells Problem: Traditional assignment strategies using triple resonance experiments (13C,15N,1H) don’t work Klein-Seetharaman et al. (2004) PNAS 101, 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

80 Traditional Solution NMR Approaches
Problems with full-length membrane proteins in detergents Detergent signals cause dynamic range problems (Detergent signals cause spectral overlap) Detergent deuteration is often not feasible Problem: 1H,1H NOESY spectra do not show protein signals 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

81 1H Chemical Shift [ppm] 1H Chemical Shift [ppm]
Evidence I: Selective excitation A. Selective excitation of the NH region using 90 degree pulse followed by direct observation. B. Selective excitation of the same region as in A. Using excitation sculpting. Backbone NH Tryptophan side chain NH 20 15 10 5 10 5 -5 1H Chemical Shift [ppm] 1H Chemical Shift [ppm] 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

82 You might also want to develop your own biophysical approaches…
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

83 Large-scale expression system for rhodopsin: HEK293 cells
19F NMR Spectroscopy Approach Advantages of 19F NMR no natural background in proteins high sensitivity sensitive to differences in environment General Method for Attachment of 19F Label Rho SH +Dithiodipyridine Rho S S N Disulfide CF CH SH Exchange When people hear “solution NMR spectroscopy of a membrane protein” – the first thing to say is “That’s not possible.” At the time there was no precedent for a study of a GPCR protein. Limitation was believed to be that of size (48kD + micelles), fast relaxation of the signals. So the first thing I did, was to demonstrate that NMR was at all applicable to the study of rhodopsin. I did this using a 19F NMR approach. Advantages… I had to develop a method for attachment using cysteine mutants… These studies were only made possible by the development of a large-scale expression system by Phil Reeves in my advisor’s laboratory. We could prepare on the order of 10mg per sample of mutant. 3 2 (TET) Rho S S CH CF 2 3 Large-scale expression system for rhodopsin: HEK293 cells 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

84 19F NMR Spectroscopy Qualitative Changes dark light Dark Light (3')
23' 43' 63' 10.5 10.0 9.5 9.0 8.5 Chemical shift (ppm relative to TFA) Here are the results. Distinct chemical shifts. Distinct changes. This demonstrates that 19F solution NMR is indeed applicable to study of rhodopsin. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

85 19F-19F Nuclear Overhauser Effect
Proximity 19F-19F Nuclear Overhauser Effect S S CH CF 2 3 S S CH CF 2 3 Rho How can we use NMR to obtain more quantitative information? One approach that contains distance information: NOE. NOE between two 19F labels, indeed observed. 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

86 Magic angle spinning http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

87 Enhancing resolution in solid state NMR
Aligned: Magic angle spinning: 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

88 Current Status of solid state NMR
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

89 REDOR http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf 9/22/2018
Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

90 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

91 The components of an NMR spectrometer
A magnet Probehead(s) Radiofrequency sources Amplifiers Analog to digital converters The lock system The shim system A computer 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

92 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

93 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

94 The Shim System http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

95 Monitoring Shimming http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

96 Monitoring shimming http://www.oci.unizh.ch/group.pages/zerbe/NMR.pdf
9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

97 Objectives of this Lecture and Practicum
Resources Physical principle Sample requirements Parameters that are measured by NMR Dynamics by NMR Limitations Practical aspects Setup of NMR experiments (downstairs) Bring your coats!! 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

98 Outlook for next week Lecture: The structure determination pipeline
Practical analysis of NMR data in computer lab: Topspin NMRpipe NMRviewJ 9/22/2018 Computational Biology Laboratory Course – Klein-Seetharaman – NMR Lecture

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