The Application and Development of Software Tools for the Processing and Analysis of Heteronuclear Multi-Dimensional NMR Data

  • E. T. Olejniczak
  • H. L. Eaton
  • E. R. P. Zuiderweg
  • S. W. Fesik
Part of the NATO ASI Series book series (NSSA, volume 225)


Software for the processing and analysis of multi-dimensional data will be described. A variety of tools has been implemented to improve the processing of multi-dimensional data including the ability to extrapolate the time domain data with linear prediction. Predicting several additional time domain points can significantly improve the data of multi-dimensional NMR experiments where one of the dimensions is undersampled due to accumulation time constraints. This data extrapolation allows a more favorable windowing of the data before Fourier transformation. It will be shown that when used conservatively the augmented data are very reliable and that improved resolution and sensitivity are obtained in the frequency domain data.

For analyzing multi-dimensional data, sorting through and keeping track of data are important, but mundane, problems that are ideally suited for a computer. Peak-picking is one important aspect of the bookkeeping that is needed. Our implementation of a peak-picking algorithm that uses volume integrals instead of heights will be described.

The analysis of multi-dimensional data is greatly facilitated if the computer has the ability to present the user with information about slices in the data set that fulfill criteria defined by the spectroscopist. For example, a useful criterion for finding adjacent or nearby amides in alpha helical sections of proteins, is to find different amide protons that have a large number of NOEs to protons at common frequencies. The computer can sort through the 3D 15 N1 H1 H NOE data and find the amide protons that best match this criterion, and then present the user with the top choices. This simple aid greatly facilitates the assignment of these amides in the spectra. Examples of this software aid will be demonstrated using simulated BPTI data.


Linear Prediction Amide Proton Time Domain Data Linear Prediction Coefficient Frequency Domain Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • E. T. Olejniczak
    • 1
  • H. L. Eaton
    • 1
  • E. R. P. Zuiderweg
    • 1
  • S. W. Fesik
    • 1
  1. 1.Pharmaceutical DiscoveryAbbott LaboratoriesAbbott ParkUSA

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