Abstract: Anthrax is an infectious disease caused by Bacillus anthracis, a Gram-positive, rod-shaped, anaerobic bacterium. The lethal factor (LF) enzyme is secreted by B. anthracis as part of a tripartite exotoxin and is chiefly responsible for anthrax-related cytotoxicity. As LF can remain in the system long after antibiotics have eradicated B. anthracis from the body, the preferred therapeutic modality would be the administration of antibiotics together with an effective LF inhibitor. Although LF has garnered a great deal of attention as an attractive target for rational drug design, relatively few published inhibitors have demonstrated activity in cell-based assays and, to date, no LF inhibitor is available as a therapeutic or preventive agent. Here we present a novel in silico high-throughput virtual screening protocol that successfully identified 5 non-hydroxamic acid small molecules as new, preliminary LF inhibitor scaffolds with low micromolar inhibition against that target, resulting in a 12.8% experimental hit rate. This protocol screened approximately 35 million nonredundant compounds for potential activity against LF and comprised topomeric searching, docking and scoring, and drug-like filtering. Among these 5 hit compounds, none of which has previously been identified as a LF inhibitor, three exhibited experimental IC(50) values less than 100 microM. These three preliminary hits may potentially serve as scaffolds for lead optimization as well as templates for probe compounds to be used in mechanistic studies. Notably, our docking simulations predicted that these novel hits are likely to engage in critical ligand-receptor interactions with nearby residues in at least two of the three (S1', S1-S2, and S2') subsites in the LF substrate binding area. Further experimental characterization of these compounds is in process. We found that micromolar-level LF inhibition can be attained by compounds with non-hydroxamate zinc-binding groups that exhibit monodentate zinc chelation as long as key hydrophobic interactions with at least two LF subsites are retained.

Abstract: Classifying objects that are sampled jointly from two or more domains has many applications. The tensor product feature space is useful for modeling interactions between feature sets in different domains but feature selection in the tensor product feature space is challenging. Conventional feature selection methods ignore the structure of the feature space and may not provide the optimal results. In this paper we propose methods for selecting features in the original feature spaces of different domains. We obtained sparsity through two approaches, one using integer quadratic programming and another using L1-norm regularization. Experimental studies on biological data sets validate our approach.

Abstract: We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

Abstract: BACKGROUND: Two genes are called synthetic lethal (SL) if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods. RESULTS: In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes) in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases) as well as on existent approved drugs (DrugBank database) supports our selection of cancer-therapy candidates. CONCLUSIONS: Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.

Abstract: BACKGROUND: The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets. RESULTS: We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets. CONCLUSION: The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.

Abstract: BACKGROUND: Human immunodeficiency virus-1 (HIV-1) has a minimal genome of only 9 genes, which encode 15 proteins. HIV-1 thus depends on the human host for virtually every aspect of its life cycle. The universal language of communication in biological systems, including between pathogen and host, is via signal transduction pathways. The fundamental units of these pathways are protein protein interactions. Understanding the functional significance of HIV-1, human interactions requires viewing them in the context of human signal transduction pathways. RESULTS: Integration of HIV-1, human interactions with known signal transduction pathways indicates that the majority of known human pathways have the potential to be effected through at least one interaction with an HIV-1 protein at some point during the HIV-1 life cycle. For each pathway, we define simple paths between start points (i.e. no edges going into a node) and end points (i.e. no edges leaving a node). We then identify the paths that pass through human proteins that interact with HIV-1 proteins. We supplement the combined map with functional information, including which proteins are known drug targets and which proteins contribute significantly to HIV-1 function as revealed by recent siRNA screens. We find that there are often alternative paths starting and ending at the same proteins but circumventing the intermediate steps disrupted by HIV-1. CONCLUSION: A mapping of HIV-1, human interactions to human signal transduction pathways is presented here to link interactions with functions. We proposed a new way of analyzing the virus host interactions by identifying HIV-1 targets as well as alternative paths bypassing the HIV-1 targeted steps. This approach yields numerous experimentally testable hypotheses on how HIV-1 function may be compromised and human cellular function restored by pharmacological approaches. We are making the full set of pathway analysis results available to the community.

Abstract: CYP2D6 substrate status is a critical Go/No Go decision criteria in central nervous system (CNS) drug discovery efforts because the polymorphic nature of CYP2D6 can lead to variable patient safety and drug efficacy. In addition, CYP2D6 is disproportionately involved in the metabolism of CNS drugs compared with other drug classes. Therefore, identifying trends in small molecule properties of CNS-penetrant compounds that can help discriminate potential CYP2D6 substrates from nonsubstrates would allow additional prioritization in the synthesis and biological evaluation of new therapeutic candidates. We report here the conversion of the CNS drug minaprine from substrate to nonsubstrate, as well as the conversion of the related CNS drug minozac from nonsubstrate to substrate, through the use of analog synthesis and CYP2D6 enzyme kinetic analyses. No single molecular property strongly correlated with substrate status for this 3-amino-4-methyl-6-phenylpyridazine scaffold, although molecular volume and charge appeared to be indirectly related. A parsed database of CYP2D6 substrates across diverse chemical structures was assembled and analyzed for physical property trends correlating with substrate status. We found that a complex interplay of properties influenced CYP2D6 substrate status and that the particular chemical scaffold affects which properties are most prominent. The results also identified an unexpected issue in CNS drug discovery, in that some property trends correlative with CYP2D6 substrates overlap previously reported properties that correlate with CNS penetrance. These results suggest the need for a careful balance in the design and synthesis of new CNS therapeutic candidates to avoid CYP2D6 substrate status while maintaining CNS penetrance.

Abstract: Cancer genomes contain many aberrant gene fusions-a few that drive disease and many more that are nonspecific passengers. We developed an algorithm (the concept signature or 'ConSig' score) that nominates biologically important fusions from high-throughput data by assessing their association with 'molecular concepts' characteristic of cancer genes, including molecular interactions, pathways and functional annotations. Copy number data supported candidate fusions and suggested a breakpoint principle for intragenic copy number aberrations in fusion partners. By analyzing lung cancer transcriptome sequencing and genomic data, we identified a novel R3HDM2-NFE2 fusion in the H1792 cell line. Lung tissue microarrays revealed 2 of 76 lung cancer patients with genomic rearrangement at the NFE2 locus, suggesting recurrence. Knockdown of NFE2 decreased proliferation and invasion of H1792 cells. Together, these results present a systematic analysis of gene fusions in cancer and describe key characteristics that assist in new fusion discovery.

Abstract: The NIAID-funded Seattle Structural Genomics Center for Infectious Disease (SSGCID) is a consortium established to apply structural genomics approaches to potential drug targets from NIAID priority organisms for biodefense and emerging and re-emerging diseases. The mission of the SSGCID is to determine approximately 400 protein structures over the next five years. In order to maximize biomedical impact, ligand-based drug-lead discovery campaigns will be pursued for a small number of high-impact targets. Here we review the center's target selection processes, which include pro-active engagement of the infectious disease research and drug therapy communities to identify drug targets, essential enzymes, virulence factors and vaccine candidates of biomedical relevance to combat infectious diseases. This is followed by a brief overview of the SSGCID structure determination pipeline and ligand screening methodology. Finally, specifics of our resources available to the scientific community are presented. Physical materials and data produced by SSGCID will be made available to the scientific community, with the aim that they will provide essential groundwork benefiting future research and drug discovery.

Abstract: The ultimate goal of bioinformatics or computational chemical biology is the sequence-based prediction of protein functionality. However, due to the degeneracy of the primary sequence code there is no unambiguous relationship. The degeneracy can be partly lifted by going to higher levels of abstraction and, for example, incorporating 3D structural information. However, sometimes even at this conceptual level functional ambiguities often remain. Here a novel conceptual framework is described (the protein meta-structure). At this level of abstraction, the protein structure is viewed as an intricate network of interacting residues. This novel conception offers unique possibilities for chemical (molecular) biology, structural genomics and drug discovery. In this review some prototypical applications will be presented that serve to illustrate the potential of the methodology.