Gronwald, Wolfram and Moussa, Sherif and Elsner, Ralph and Jung, Astrid and Ganslmeier, Bernhard and Trenner, Jochen and Kremer, Werner and Neidig, Klaus-Peter and Kalbitzer, Hans Robert (2002) Automated assignment of NOESY NMR spectra using a knowledge based method (KNOWNOE). JOURNAL OF BIOMOLECULAR NMR, 23 (4). pp. 271-287. ISSN 0925-2738
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Automated assignment of NOESY spectra is a prerequisite for automated structure determination of biological macromolecules. With the program KNOWNOE we present a novel, knowledge based approach to this problem. KNOWNOE is devised to work directly with the experimental spectra without interference of an expert. Besides making use of routines already implemented in AUREMOL, it contains as a central part a knowledge driven Bayesian algorithm for solving ambiguities in the NOE assignments. These ambiguities mainly arise from chemical shift degeneration which allows multiple assignments of cross peaks. Using a set of 326 protein NMR structures, statistical tables in the form of atom-pairwise volume probability distributions (VPDs) were derived. VPDs for all assignment possibilities relevant to the assignments of interproton NOEs were calculated. With these data for a given cross peak with N possible assignments A(i) (i = 1,...,N) the conditional probabilities P(A(i), a|V-0 ) can be calculated that the assignment A(i) determines essentially all (a-times) of the cross peak volume V-0. An assignment A(k) with a probability P(A(k), a|V-0) higher than 0.8 is transiently considered as unambiguously assigned. With a list of unambiguously assigned peaks a set of structures is calculated. These structures are used as input for a next cycle of iteration where a distance threshold D-max is dynamically reduced. The program KNOWNOE was tested on NOESY spectra of a medium size protein, the cold shock protein (TmCsp) from Thermotoga maritima. The results show that a high quality structure of this protein can be obtained by automated assignment of NOESY spectra which is at least as good as the structure obtained from manual data evaluation.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | NUCLEAR-MAGNETIC-RESONANCE; CORRECTING DISTANCE GEOMETRY; SIGNAL CLASS RECOGNITION; PROTEIN STRUCTURES; CORRELATED SPECTROSCOPY; PEAK INTEGRATION; BAYESIAN METHOD; PROGRAM; DOMAIN; |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Dr. Hans Robert Kalbitzer |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 12 Oct 2021 06:26 |
| Last Modified: | 12 Oct 2021 06:26 |
| URI: | https://pred.uni-regensburg.de/id/eprint/40039 |
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