The aim of this work is to present the anchor-GRIND methodology. Anchor-GRIND efficiently combines a priori chemical and biological knowledge about the studied compounds with alignment-independent molecular descriptors derived from molecular interaction fields. Such descriptors are particularly useful for series of ligands sharing a common scaffold but with very diverse substituents. The method uses a specific position of the molecular structure (the "anchor point") to compare the spatial distribution of the molecular interaction fields of the substituents. The descriptors produced are more detailed and specific than the original GRIND while still avoiding the bias introduced by the alignment. Three data sets have been studied to demonstrate the usefulness of the anchor-GRIND methodology for 3D-QSAR modeling. The two first data sets respectively include congeneric series of the hepatitis C virus NS3 protease and of the acetylcholinesterase inhibitors. The third data set discriminates between factor Xa inhibitors of high and low affinity. In all the series presented, the models obtained with the anchor-GRIND are statistically sound and easy to interpret.

The identification of metabolites is almost exclusively done with liquid chromatography/tandem mass spectrometry (LC/MSMS) and despite the enormous progress in the development of these techniques and software for handling of data this is a time-consuming task. In this study the use of quadrupole time-of-flight (QTOF)-generated MS(E) and MS/MS data were compared with respect to rationalization of metabolites. In addition Mass-MetaSite, a semi-automated software for metabolite identification, was evaluated. The program combines the information from MS raw data, in the form of collision-induced dissociation spectra, with a prediction of the site of metabolism in order to assign the structure of a metabolite. The aim of the software is to mimic the rationalization of fragment ions performed by a biotransformation scientist in the process of structural elucidation. For this evaluation, metabolite identification in human liver microsomes was accomplished for 19 commercially available compounds and 15 in-house compounds. The results were very encouraging and for 96% of the metabolites the same structures were assigned using MS(E) compared with MSMS acquired data. The possibility of using MS(E) could considerably reduce the analysis time. Moreover, Mass-MetaSite performed well and the correct assigned structure, compared to manual inspection of the data, was picked in the first rank in ∼80% of the cases. In conclusion MS(E) could be successfully used for metabolite identification in order to reduce time of analysis and Mass-MetaSite could alleviate the work of a biotransformation scientist and decrease the workload by assigning the structure for a majority of the metabolites.

The specificity and kinetics of the interaction between the pathogenesis-related group of thaumatin-like proteins (PR5) in higher plants and (1,3)-b-d-glucans have been investigated. Two thaumatin-like proteins with 60% aminoacid sequence identity were purified from extracts of germinated barley grain, and were designated HvPR5b and HvPR5c. Purified