Background: Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding.

Human APOBEC3 (A3) proteins form part of the intrinsic immunity to retroviruses. Carrying 1 or 2 copies of a cytidine deaminase motif, A3s act by deamination of retroviral genomes during reverse transcription. HIV-1 overcomes this inhibition by the Vif protein, which prevents incorporation of A3 into virions. In this study we modeled and probed the structure of APOBEC3C (A3C), a singledomain A3 with strong antilentiviral activity. The 3-dimensional protein model was used to predict the effect of mutations on antiviral activity, which was tested in a ⌬vif simian immunodeficiency virus (SIV) reporter virus assay. We found that A3C activity requires protein dimerization for antiviral activity against SIV. Furthermore, by using a structure-based algorithm for automated pocket extraction, we detected a putative substrate binding pocket of A3C distal from the zinc-coordinating deaminase motif. Mutations in this region diminished antiviral activity by excluding A3C from virions. We found evidence that the small 5.8S RNA specifically binds to this locus and mediates incorporation of A3C into virus particles.Bioinformatics ͉ immunodeficiency ͉ protein structure ͉ proteinϪprotein interaction ͉ retrovirus

Identification of potential ligand-binding pockets is an initial step in receptor-based drug design. While many geometric or energy-based binding-site prediction methods characterize the size and shape of protein cavities, few of them offer an estimate of the pocket's ability to bind small drug-like molecules. Here, we present a shape-based technique to examine binding-site druggability from the crystal structure of a given protein target. The method includes the PocketPicker algorithm to determine putative binding-site volumes for ligand-interaction. Pocket shape descriptors were calculated for both known ligand binding sites and empty pockets and were then subjected to self-organizing map clustering. Descriptors were calculated for structures derived from a database of representative drug-protein complexes with experimentally determined binding affinities to characterize the "druggable pocketome". The new method provides a means for selecting drug targets and potential ligand-binding pockets based on structural considerations and addresses orphan binding sites.

Patterns of receptor-ligand interaction can be conserved in functionally equivalent proteins even in the absence of sequence homology. Therefore, structural comparison of ligand-binding pockets and their pharmacophoric features allow for the characterization of so-called "orphan" proteins with known three-dimensional structure but unknown function, and predict ligand promiscuity of binding pockets. We present an algorithm for rapid pocket comparison (PoLiMorph), in which protein pockets are represented by self-organizing graphs that fill the volume of the cavity. Vertices in these three-dimensional frameworks contain information about the local ligand-receptor interaction potential coded by fuzzy property labels. For framework matching, we developed a fast heuristic based on the maximum dispersion problem, as an alternative to techniques utilizing clique detection or geometric hashing algorithms. A sophisticated scoring function was applied that incorporates knowledge about property distributions and ligand-receptor interaction patterns. In an all-against-all virtual screening experiment with 207 pocket frameworks extracted from a subset of PDBbind, PoLiMorph correctly assigned 81% of 69 distinct structural classes and demonstrated sustained ability to group pockets accommodating the same ligand chemotype. We determined a score threshold that indicates "true" pocket similarity with high reliability, which not only supports structure-based drug design but also allows for sequence-independent studies of the proteome.

The IAPs are key regulators of the apoptotic pathways and are commonly overexpressed in many cancer cells. IAPs contain one to three BIR domains that are crucial for their inhibitory function. The pro-survival properties of XIAP come from binding of the BIR domains to the pro-apoptotic caspases. The BIR3 domain of XIAP binds and inhibits caspase 9, while the BIR2 domain binds and inhibits the terminal caspases 3 and 7. While XIAP BIR3 inhibitors have previously been reported, they also inhibit cIAP1/2 and promote the release of TNFα, potentially limiting their therapeutic utility. This paper will focus on the optimization of selective XIAP BIR2 inhibitors leading to the discovery of highly potent benzodiazepinone 36 (IC50 = 45 nM), which has high levels of selectivity over XIAP BIR3 and cIAP1 BIR2/3 and shows efficacy in a xenograft pharmacodynamic model monitoring caspase activity while not promoting the release of TNFα in vitro.