Modern NMR spectroscopy of proteins and peptides in solution and its relevance to drug design

The knowledge of the three-dimensional (3D) structures and conformational dynamics of proteins and peptides is important for the understanding of biochemical and genetic data derived for these molecules. This understanding can ultimately be of help in drug design. We describe here the role of Nuclear Magnetic Resonance (NMR) spectroscopy in this process for three distinct situations: for small proteins, where relatively simple NMR methods can be used for full 3D structure determination; for larger proteins that require multinuclear multidimensional NMR but for which full 3D structures can still be obtained; and for small peptides that are studied in interaction with macromolecules (receptors) using specialized NMR techniques. A fourth situation, pertaining to large systems where only partial structural information can be obtained from NMR data, is briefly discussed. Molecules of interest to the biomedical field (C5a and stromelysin) are discussed as examples.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43356/1/11091_2005_Article_BF02174537.pd

Catalysis of serine oligopeptidases is controlled by a gating filter mechanism

Proteases have a variety of strategies for selecting substrates in order to prevent uncontrolled protein degradation. A recent crystal structure determination of prolyl oligopeptidase has suggested a way for substrate selection involving an unclosed seven-bladed β-propeller domain. We have engineered a disulfide bond between the first and seventh blades of the propeller, which resulted in the loss of enzymatic activity. These results provided direct evidence for a novel strategy of regulation in which oscillating propeller blades act as a gating filter during catalysis, letting small peptide substrates into the active site while excluding large proteins to prevent accidental proteolysis

AUREMOL-RFAC-3D, combination of R-factors and their use for automated quality assessment of protein solution structures

We present here the computer program AUREMOL-RFAC-3D that is a generalization of the previously published program RFAC for the fully automated estimation of residual indices (R-factors) from 2D NOESY spectra. It is part of the larger AUREMOL software package (www.auremol.de). RFAC-3D calculates R-factors directly from two-dimensional homonuclear NOESY spectra as well as from three-dimensional (15)N or (13)C edited NOESY-HSQC spectra and thus extends the application range to larger proteins. The fully automated method includes automated peak picking and integration, a Bayesian noise and artifact recognition and the use of the complete relaxation matrix formalism. To enhance the reliability of the calculated R-factors the method is also generalized to calculate combined R-factors from a set of 2D and 3D-spectra. For an optimal combination of the information derived from different sources a plausible formalism had to be derived. In addition, we present a novel direct R-factors based measure that correlates an R-factors as defined in this paper to the root mean square deviation of the actual structure from the optimal structure. The new program has been successfully tested on the histidine containing phosphocarrier protein (HPr) from Staphylococcus carnosus and on the Ras-binding domain (RBD) of the Ral guanine-nucleotide dissociation stimulation factor (RalGDS)