Robert Peterson Group Meeting 1/25/11 Topspin 3 New Features Protein Dynamic Center Non Uniform Sampling A few older features Licensing

First things first: We can’t use Topspin 3 on our instruments! It requires AV2 or AV3. *Even Bosch is too old! (it’s an AV1) So why talk about it? 1)We can use the processing features 2)Possibly we could find a justification to upgrade our consoles

Protein Dynamic Center

Interlude: alternative schemes for processing NMR data There are several recently developed schemes for acquiring and processing NMR data. These each employ tricks that enable multidimensional data to be acquired in less time than conventional FT NMR. And they employ several alternate processing schemes that generally allow better resolution in the indirect dimensions. They typically use some kind of sparse sampling to get large numbers of points in indirect dimensions without increasing the experiment time. Bruker has packaged many of these schemes into TopSpin 3.

Uniform sampling Sparse sampling 1

Multi-Dimensional Decomposition (MDD) In this method, the signal: Is “decomposed” into R components: Which are the tensor product of three one-dimensional shapes: These shapes can be used to reconstruct the spectrum:

Maximum Entropy Rowland Toolkit: Forward Maximum Entropy Azara (CCPN)

Maximum Entropy Maximum Entropy Reconstruction is a general method in which the spectrum f that has the highest entropy is reconstructed: Subject to the constraint that the inverse FT of the spectrum is consistent with the measured FID: The parameters can be obtained automatically now, making the whole business more user-friendly:

Maximum Entropy Jeffrey Hoch and coworkers systematically compared several methods of fast nD NMR Journal of Magnetic Resonance 182 (2006) For example: radial sampling. Processed using projection reconstruction or with ME, these sampling schemes gave artifacts related to the sampling regularity. Their main conclusion was that a random sampling scheme was always better (for example, sparse random sampling with exponential weighting). Also, ME worked better than other processing methods (but it was never compared directly with MDD).

Sparse Multidimensional Fourier Transformation Uses completely random sampling (i.e. not on a grid). Therefore different time/frequency dimensions are not separable as in conventional processing. Instead uses an approximation of a truly multidimensional Fourier transform. But only performs the Fourier integral on regions that contain signal. Lowest signal-to-artifact (S/A) ration. Example Acquire high resolution HNCO - use to get a list of all resonances Acquire 5D HC(CC-TOCSY)CONH - process with SMFT Fourier integral is calculated in regions of interest only In this case, you get a series of 2D H-C slices, each containing all the aliphatic protons and carbons from one residue.

Covariance NMR Processing for 2D homonuclear experiments (or 2D planes within larger dimensionality experiments). Spectra are fully symmetric. Resolution in both dimensions is determined by the high resolution in the direct dimension.

Summary:  Reduced dimensionality is a special case of non uniform sampling.  Random sampling is generally superior to regular sampling of any kind.  There are many different processing schemes. Each has advantages and disadvantages.  Up until now, these are very difficult to implement. It sure would be nice to be able to do some of these things easily.

Note: this is with linear prediction set up in F1 and F2 only (in edp)