title: Automated assignment of NOESY NMR spectra using a knowledge based method (KNOWNOE).
abstract: 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 Ai (i = 1,...,N) the conditional probabilities P(Ai, a/V0) can be calculated that the assignment Ai determines essentially all (a-times) of the cross peak volume V0. An assignment Ak with a probability P(Ak, a/V0) 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 Dmax 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.
authors.affiliation: Department of Biophysics and Physical Biochemistry, University of Regensburg, Federal Republic of Germany.
authors.forename: Wolfram
authors.initials: W
authors.lastname: Gronwald
authors.forename: Sherif
authors.initials: S
authors.lastname: Moussa
authors.forename: Ralph
authors.initials: R
authors.lastname: Elsner
authors.forename: Astrid
authors.initials: A
authors.lastname: Jung
authors.forename: Bernhard
authors.initials: B
authors.lastname: Ganslmeier
authors.forename: Jochen
authors.initials: J
authors.lastname: Trenner
authors.forename: Werner
authors.initials: W
authors.lastname: Kremer
authors.forename: Klaus-Peter
authors.initials: KP
authors.lastname: Neidig
authors.forename: Hans Robert
authors.initials: HR
authors.lastname: Kalbitzer
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001426:Bacterial Proteins / Q000737:chemistry; D030562:Databases, Protein; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D001426:Bacterial Proteins; D011506:Proteins