Binding inhibitors of the bacterial sliding clamp by design.

The bacterial replisome is a target for the development of new antibiotics to combat drug resistant strains. The ß(2) sliding clamp is an essential component of the replicative machinery, providing a platform for recruitment and function of other replisomal components and ensuring polymerase processivity during DNA replication and repair. A single binding region of the clamp is utilized by its binding partners, which all contain conserved binding motifs. The C-terminal Leu and Phe residues of these motifs are integral to the binding interaction. We acquired three-dimensional structural information on the binding site in ß(2) by a study of the binding of modified peptides. Development of a three-dimensional pharmacophore based on the C-terminal dipeptide of the motif enabled identification of compounds that on further development inhibited α-ß(2) interaction at low micromolar concentrations. We report the crystal structure of the complex containing one of these inhibitors, a biphenyl oxime, bound to ß(2), as a starting point for further inhibitor design.

Toward novel universal descriptors: charge fingerprints.

Although there are a myriad of molecular descriptors for QSAR described in the literature, many descriptors contain similar information as others or are information poor. Recent work has suggested that it may be possible to discover a relatively small pool of 'universal' descriptors from which subsets can be drawn to build a diverse variety of models. We describe a new type of descriptor of this type, the charge fingerprint. This descriptor family can build good QSAR models of a diverse range of physicochemical and biological properties and can be calculated quickly and easily. It appears to be useful for modeling large data sets and has potential for screening large virtual libraries.

Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural network.

Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.