Abstract: In the current work, we present a hydrogen-bond analysis of 2673 ligand-receptor complexes that suggests the total number of hydrogen bonds formed between a ligand and its receptor is a poor predictor of ligand potency; furthermore, even that poor prediction does not suggest a statistically significant correlation between hydrogen-bond formation and potency. While we are not the first to suggest that hydrogen bonds on average do not generally contribute to ligand binding affinities, this additional evidence is nevertheless interesting. The primary role of hydrogen bonds may instead be to ensure specificity, to correctly position the ligand within the active site, and to hold the protein active site in a ligand-friendly conformation. We also present a new computer program called HBonanza (hydrogen-bond analyzer) that aids the analysis and visualization of hydrogen-bond networks. HBonanza, which can be used to analyze single structures or the many structures of a molecular dynamics trajectory, is open source and python implemented, making it easily editable, customizable, and platform independent. Unlike many other freely available hydrogen-bond analysis tools, HBonanza provides not only a text-based table describing the hydrogen-bond network, but also a Tcl script to facilitate visualization in VMD, a popular molecular visualization program. Visualization in other programs is also possible. A copy of HBonanza can be obtained free of charge from http://www.nbcr.net/hbonanza.

Abstract: Mounting evidence thus far indicates that human cytochrome P450 2B6 (CYP2B6), an enzyme expressed at a relatively low level functionally, is primarily responsible for the metabolism of several clinically relevant drugs, including propofol, efavirenz, bupropion, mephobarbital, and the propofol analog 2,6-di-sec-butyl phenol. We used molecular dynamics and molecular docking methods to predict such interactions and to compare with experimentally measured metabolisms. Insight II and Discover Studio 2.5 were used to carry out the docking of these substrates into CYP2B6 to explore the critical residues and interaction energies of the complexes. Phe297, Glu301, Thr302 and Val367 were identified as major drug-binding residues, which is consistent with previous data on site-directed mutagenesis, crystallography structure, and from modeling and docking studies. In addition, our docking results suggest that nonpolar amino acid clusters and heme also participate in binding to mediate drug oxidative metabolism. The binding modes of the five clinically relevant substrates mentioned above for metabolism on CYP2B6 are presented.

Abstract: Funded by the National Institute of Allergy and Infectious Diseases, the Pathosystems Resource Integration Center (PATRIC) is a genomics-centric relational database and bioinformatics resource designed to assist scientists in infectious-disease research. Specifically, PATRIC provides scientists with (i) a comprehensive bacterial genomics database, (ii) a plethora of associated data relevant to genomic analysis, and (iii) an extensive suite of computational tools and platforms for bioinformatics analysis. While the primary aim of PATRIC is to advance the knowledge underlying the biology of human pathogens, all publicly available genome-scale data for bacteria are compiled and continually updated, thereby enabling comparative analyses to reveal the basis for differences between infectious free-living and commensal species. Herein we summarize the major features available at PATRIC, dividing the resources into two major categories: (i) organisms, genomes, and comparative genomics and (ii) recurrent integration of community-derived associated data. Additionally, we present two experimental designs typical of bacterial genomics research and report on the execution of both projects using only PATRIC data and tools. These applications encompass a broad range of the data and analysis tools available, illustrating practical uses of PATRIC for the biologist. Finally, a summary of PATRIC's outreach activities, collaborative endeavors, and future research directions is provided.

Abstract: BACKGROUND: Glioblastoma multiforme (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. Identification of molecular interactions that associate with disease progression may be key in finding novel treatments. METHODS: Using five independent molecular and clinical datasets with a set of computational algorithms we were able to identify a gene-gene and gene-microRNA network that significantly stratifies patient prognosis. By combining gene expression microarray data with microRNA expression levels, copy number alterations, drug response and clinical data, combined with network knowledge, we were able to identify a single pathway at the core of glioblastoma. RESULTS: This network, the p38 network, and an associated microRNA, hsa-miR-9, facilitate prognostic stratification. The microRNA hsa-miR-9 correlated with network behavior and presents binding affinities with network members in a manner that suggests control over network behavior. A similar control over network behavior is possible through a set of drugs. These drugs are part of the treatment regimen for a subpopulation of the patients that participated in the TCGA study and for which the study provides clinical information. Interestingly, the patients that were treated with these specific sets of drugs, all of which targeted against p38 network members, demonstrate highly significant stratification of prognosis. CONCLUSIONS: Combined, these results call for attention to p38 network targeted treatment and present the p38 network-hsa-miR-9 control mechanism as critical in GBM progression.

Abstract: Blockade of human ether-a-go-go related gene (hERG) channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining quantitative structure-activity relationship (QSAR) modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high-predictive capacity and improved enrichment and could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed, and molecular docking studies were conducted, resulting in high correlations (R(2) = 0.81) between predicted and experimental pIC(5)(0)s. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities.

Abstract: Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.

Abstract: BACKGROUND: Each organ has a specific function in the body. "Organ-specificity" refers to differential expressions of the same gene across different organs. An organ-specific gene/protein is defined as a gene/protein whose expression is significantly elevated in a specific human organ. An "organ-specific marker" is defined as an organ-specific gene/protein that is also implicated in human diseases related to the organ. Previous studies have shown that identifying specificity for the organ in which a gene or protein is significantly differentially expressed, can lead to discovery of its function. Most currently available resources for organ-specific genes/proteins either allow users to access tissue-specific expression over a limited range of organs, or do not contain disease information such as disease-organ relationship and disease-gene relationship. RESULTS: We designed an integrated Human Organ-specific Molecular Electronic Repository (HOMER, http://bio.informatics.iupui.edu/homer), defining human organ-specific genes/proteins, based on five criteria: 1) comprehensive organ coverage; 2) gene/protein to disease association; 3) disease-organ association; 4) quantification of organ-specificity; and 5) cross-linking of multiple available data sources.HOMER is a comprehensive database covering about 22,598 proteins, 52 organs, and 4,290 diseases integrated and filtered from organ-specific proteins/genes and disease databases like dbEST, TiSGeD, HPA, CTD, and Disease Ontology. The database has a Web-based user interface that allows users to find organ-specific genes/proteins by gene, protein, organ or disease, to explore the histogram of an organ-specific gene/protein, and to identify disease-related organ-specific genes by browsing the disease data online.Moreover, the quality of the database was validated with comparison to other known databases and two case studies: 1) an association analysis of organ-specific genes with disease and 2) a gene set enrichment analysis of organ-specific gene expression data. CONCLUSIONS: HOMER is a new resource for analyzing, identifying, and characterizing organ-specific molecules in association with disease-organ and disease-gene relationships. The statistical method we developed for organ-specific gene identification can be applied to other organism. The current HOMER database can successfully answer a variety of questions related to organ specificity in human diseases and can help researchers in discovering and characterizing organ-specific genes/proteins with disease relevance.

Abstract: Drug structures may be quantitatively compared based on 2D topological structural considerations and based on 3D characteristics directly related to binding. A framework for combining multiple similarity computations is presented along with its systematic application to 358 drugs with overlapping pharmacology. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, or their combination. For prediction of primary targets, the benefit of 3D over 2D was relatively small, but for prediction of off-targets, the added benefit was large. In addition to assessing prediction, the relationship between chemical similarity and pharmacological novelty was studied. Drug pairs that shared high 3D similarity but low 2D similarity (i.e., a novel scaffold) were shown to be much more likely to exhibit pharmacologically relevant differences in terms of specific protein target modulation.

Abstract: BACKGROUND: As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI) network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions. RESULTS: We constructed a weighted human PPI network (WHPN) using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN) was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching PubMed manually. We found that 31 of the optimized genes were targeted as drug response markers in DrugBank. CONCLUSIONS: Here we have shown that network theory combined with epigenetic characteristics provides a favorable platform from which to identify cancer-related genes. We prioritized 154 potential cancer-related genes with aberrant methylation that might contribute to the further understanding of cancers.