Virtual database screening allows for millions of chemical compounds to be computationally selected based on structural complimentary to known inhibitors or to a target binding site on a biological macromolecule. Compound selection in virtual database screening when targeting a biological macromolecule is typically based on the interaction energy between the chemical compound and the target macromolecule. In the present study it is shown that this approach is biased toward the selection of high molecular weight compounds due to the contribution of the compound size to the energy score. To account for molecular weight during energy based screening, we propose normalization strategies based on the total number of heavy atoms in the chemical compounds being screened. This approach is computationally efficient and produces molecular weight distributions of selected compounds that can be selected to be (1) lower than that of the original database used in the virtual screening, which may be desirable for selection of leadlike compounds or (2) similar to that of the original database, which may be desirable for the selection of drug-like compounds. By eliminating the bias in target-based database screening toward higher molecular weight compounds it is anticipated that the proposed procedure will enhance the success rate of computer-aided drug design.

The initiation of transcription is regulated by transcription factors (TFs) binding to DNA response elements (REs). How do TFs recognize specific binding sites among the many similar ones available in the genome? Recent research has illustrated that even a single nucleotide substitution can alter the selective binding of TFs to coregulators, that prior binding events can lead to selective DNA binding, and that selectivity is influenced by the availability of binding sites in the genome. Here, we combine structural insights with recent genomics screens to address the problem of TF-DNA interaction specificity. The emerging picture of selective binding site sequence recognition and TF activation involves three major factors: the cellular network, protein and DNA as dynamic conformational ensembles and the tight packing of multiple TFs and coregulators on stretches of regulatory DNA. The classification of TF recognition mechanisms based on these factors impacts our understanding of how transcription initiation is regulated.

The binding of S100B to p53 down-regulates wild-type p53 tumor suppressor activity in cancer cells such as malignant melanoma, so a search for small molecules that bind S100B and prevent S100B-p53 complex formation was undertaken. Chemical databases were computationally searched for potential inhibitors of S100B, and 60 compounds were selected for testing on the basis of energy scoring, commercial availability, and chemical similarity clustering. Seven of these compounds bound to S100B as determined by steady state fluorescence spectroscopy (1.0 microM< or = K(D) < or = 120 microM) and five inhibited the growth of primary malignant melanoma cells (C8146A) at comparable concentrations (1.0 microM < or = IC(50) < or = 50 microM). Additionally, saturation transfer difference (STD) NMR experiments confirmed binding and qualitatively identified protons from the small molecule at the small molecule-S100B interface. Heteronuclear single quantum coherence (HSQC) NMR titrations indicate that these compounds interact with the p53 binding site on S100B. An NMR-docked model of one such inhibitor, pentamidine, bound to Ca(2+)-loaded S100B was calculated using intermolecular NOE data between S100B and the drug, and indicates that pentamidine binds into the p53 binding site on S100B defined by helices 3 and 4 and loop 2 (termed the hinge region).

SUMMARY The emergence of multidrug-resistant pathogens necessitates the search for new antibiotics acting on previously unexplored targets. Nicotinate mononucleotide adenylyltransferase of the NadD family, an essential enzyme of NAD biosynthesis in most bacteria, was selected as a target for structure-based inhibitor development. Using iterative in silico and in vitro screens we identified small molecule compounds that efficiently inhibited target enzymes from Escherichia coli (ecNadD) and Bacillus anthracis (baNadD) but had no effect on functionally equivalent human enzymes. On-target antibacterial activity was demonstrated for some of the selected inhibitors. A 3D structure of baNadD was solved in complex with one of these inhibitors (3_02) providing mechanistic insights and guidelines for further improvement. Most importantly, the results of this study help validate NadD as a target for the development of antibacterial agents with potential broad-spectrum activity.