ABSTRACT
The amount of experimental restraints e.g., NOEs is often too small for calculating high quality three-dimensional structures by restrained molecular dynamics. Considering this as a typical missing value problem we propose here a model based data imputation technique that should lead to an improved estimation of the correct structure. The novel automated method implemented in AUREMOL makes a more efficient use of the experimental information to obtain NMR structures with higher accuracy. It creates a large set of substitute restraints that are used either alone or together with the experimental restraints. The new approach was successfully tested on three examples: firstly, the Ras-binding domain of Byr2 from Schizosaccharomyces pombe, the mutant HPr (H15A) from Staphylococcus aureus, and a X-ray structure of human ubiquitin. In all three examples, the quality of the resulting final bundles was improved considerably by the use of additional substitute restraints, as assessed quantitatively by the calculation of RMSD values to the "true" structure and NMR R-factors directly calculated from the original NOESY spectra or the published diffraction data.
Subject(s)
Magnetic Resonance Spectroscopy/methods , Proteins/chemistry , Bacterial Proteins/chemistry , Humans , MAP Kinase Kinase Kinases/chemistry , Methods , Models, Molecular , Molecular Dynamics Simulation , Phosphoenolpyruvate Sugar Phosphotransferase System/chemistry , Protein Conformation , Schizosaccharomyces pombe Proteins/chemistry , Software , Staphylococcus aureus , Ubiquitin/chemistryABSTRACT
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.
