Abstract: Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via network-based inference (NBI) method, we further developed node- and edge-weighted NBI methods for CPI prediction here. Two comprehensive CPI bipartite networks extracted from ChEMBL database were used to evaluate the methods, one containing 17,111 CPI pairs between 4,741 compounds and 97 G protein-coupled receptors, the other including 13,648 CPI pairs between 2,827 compounds and 206 kinases. The range of the area under receiver operating characteristic curves was 0.73 to 0.83 for the external validation sets, which confirmed the reliability of the prediction. The weak-interaction hypothesis in CPI network was identified by the edge-weighted NBI method. Moreover, to validate the methods, several candidate targets were predicted for five approved drugs, namely imatinib, dasatinib, sertindole, olanzapine and ziprasidone. The molecular hypotheses and experimental evidence for these predictions were further provided. These results confirmed that our methods have potential values in understanding molecular basis of drug polypharmacology and would be helpful for drug repositioning.

Abstract: BACKGROUND: Strongyloidiasis is one of the most neglected diseases distributed worldwide with endemic areas in developed countries, where chronic infections are life threatening. Despite its impact, very little is known about the molecular biology of the parasite involved and its interplay with its hosts. Next generation sequencing technologies now provide unique opportunities to rapidly address these questions. PRINCIPAL FINDINGS: Here we present the first transcriptome of the third larval stage of S. stercoralis using 454 sequencing coupled with semi-automated bioinformatic analyses. 253,266 raw sequence reads were assembled into 11,250 contiguous sequences, most of which were novel. 8037 putative proteins were characterized based on homology, gene ontology and/or biochemical pathways. Comparison of the transcriptome of S. strongyloides with those of other nematodes, including S. ratti, revealed similarities in transcription of molecules inferred to have key roles in parasite-host interactions. Enzymatic proteins, like kinases and proteases, were abundant. 1213 putative excretory/secretory proteins were compiled using a new pipeline which included non-classical secretory proteins. Potential drug targets were also identified. CONCLUSIONS: Overall, the present dataset should provide a solid foundation for future fundamental genomic, proteomic and metabolomic explorations of S. stercoralis, as well as a basis for applied outcomes, such as the development of novel methods of intervention against this neglected parasite.

Abstract: Clofazimine is a poorly-soluble but orally-bioavailable small molecule drug that massively accumulates in macrophages when administered over prolonged periods of time. To determine whether crystal-like drug inclusions (CLDIs) that form in subcellular spaces correspond to pure clofazimine crystals, macrophages of clofazimine-fed mice were elicited with an intraperitoneal thioglycollate injection. Inside these cells, CLDIs appeared uniform in size and shape, but were sensitive to illumination. Once removed from cells, CLDIs were unstable. Unlike pure clofazimine crystals, isolated CLDIs placed in distilled water burst into small birefringent globules, which aggregated into larger clusters. Also unlike pure clofazimine crystals, CLDIs fragmented when heated, and disintegrated in alkaline media. In contrast to all other organelles, CLDIs were relatively resistant to sonication and trypsin digestion, which facilitated their biochemical isolation. The powder x-ray diffraction pattern obtained from isolated CLDIs was consistent with the diffraction pattern of liquid crystals and inconsistent with the expected molecular diffraction pattern of solid, three dimensional crystals. Observed with the transmission electron microscope (TEM), CLDIs were bounded by an atypical double-layered membrane, approximately 20 nanometers thick. CLDIs were polymorphic, but generally exhibited an internal multilayered organization, comprised of stacks of membranes 5 to 15 nanometers thick. Deep-etch, freeze-fracture electron microscopy of unfixed snap-frozen tissue samples confirmed this supramolecular organization. These results suggest that clofazimine accumulates in macrophages by forming a membrane-bound, multilayered, liquid crystal-like, semi-synthetic cytoplasmic structure.

Abstract: MOTIVATION: Mass spectrometry is a complex technique used for large-scale protein profiling with clinical and pharmaceutical applications. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a systems point of view. RESULTS: In this work, we investigate this problem by putting together the different modules in a typical proteomics work flow, in order to capture and analyze key factors that impact the number of identified peptides and quantified proteins, protein quantification error, differential expression results, and classification performance. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical bottlenecks worth investing time and resources into for improving performance. Using the model-based approach proposed here, one can study systematically the critical problem of proteomic biomarker discovery, by means of simulation using ground-truthed synthetic MS data.

Abstract: BACKGROUND: Epigenetic mechanisms mainly include DNA methylation, post-translational modifications of histones, chromatin remodeling and non-coding RNAs. All of these processes are mediated and controlled by enzymes. Abnormalities of the enzymes are involved in a variety of complex human diseases. Recently, potent natural or synthetic chemicals are utilized to establish the quantitative contributions of epigenetic regulation through the enzymes and provide novel insight for developing new therapeutics. However, the development of more specific and effective epigenetic therapeutics requires a more complete understanding of the chemical epigenomic landscape. DESCRIPTION: Here, we present a human epigenetic enzyme and modulator database (HEMD), the database which provides a central resource for the display, search, and analysis of the structure, function, and related annotation for human epigenetic enzymes and chemical modulators focused on epigenetic therapeutics. Currently, HEMD contains 269 epigenetic enzymes and 4377 modulators in three categories (activators, inhibitors, and regulators). Enzymes are annotated with detailed description of epigenetic mechanisms, catalytic processes, and related diseases, and chemical modulators with binding sites, pharmacological effect, and therapeutic uses. Integrating the information of epigenetic enzymes in HEMD should allow for the prediction of conserved features for proteins and could potentially classify them as ideal targets for experimental validation. In addition, modulators curated in HEMD can be used to investigate potent epigenetic targets for the query compound and also help chemists to implement structural modifications for the design of novel epigenetic drugs. CONCLUSIONS: HEMD could be a platform and a starting point for biologists and medicinal chemists for furthering research on epigenetic therapeutics. HEMD is freely available at http://mdl.shsmu.edu.cn/HEMD/.

Abstract: Food materials designated as "Generally Recognized as Safe" (GRAS) are attracting the attention of researchers in their attempts to systematically identify compounds with putative health-related benefits. In particular, there is currently a great deal of interest in exploring possible secondary benefits of flavor ingredients, such as those relating to health and wellness. One step in this direction is the comprehensive characterization of the chemical structures contained in databases of flavoring substances. Herein, we report a comprehensive analysis of the recently updated FEMA GRAS list of flavoring substances (discrete chemical entities only). Databases of natural products, approved drugs and a large set of commercial molecules were used as references. Remarkably, natural products continue to be an important source of bioactive compounds for drug discovery and nutraceutical purposes. The comparison of five collections of compounds of interest was performed using molecular properties, rings, atom counts and structural fingerprints. It was found that the molecular size of the GRAS flavoring substances is, in general, smaller cf. members of the other databases analyzed. The lipophilicity profile of the GRAS database, a key property to predict human bioavailability, is similar to approved drugs. Several GRAS chemicals overlap to a broad region of the property space occupied by drugs. The GRAS list analyzed in this work has high structural diversity, comparable to approved drugs, natural products and libraries of screening compounds. This study represents one step towards the use of the distinctive features of the flavoring chemicals contained in the GRAS list and natural products to systematically search for compounds with potential health-related benefits.

Abstract: Computational design of small molecule putative inhibitors of Polo-like kinase 1 (Plk1) is presented. Plk1, which regulates the cell cycle, is often over expressed in cancers. Down regulation of Plk1 has been shown to inhibit tumor progression. Most kinase inhibitors interact with the ATP binding site on Plk1, which is highly conserved. This makes the development of Plk1-specific inhibitors challenging, since different kinases have similar ATP sites. However, Plk1 also contains a unique region called the polo-box domain (PBD), which is absent from other kinases. In this study, the PBD site was used as a target for designed Plk1 putative inhibitors. Common structural features of several experimentally known Plk1 ligands were first identified. The findings were used to design small molecules that specifically bonded Plk1. Drug likeness and possible toxicities of the molecules were investigated. Molecules with no implied toxicities and optimal drug likeness values were used for docking studies. Several molecules were identified that made stable complexes only with Plk1 and LYN kinases, but not with other kinases. One molecule was found to bind exclusively the PBD site of Plk1. Possible utilization of the designed molecules in drugs against cancers with over expressed Plk1 is discussed.

Abstract: BACKGROUND: The identification of proteins by mass spectrometry is a standard method in biopharmaceutical quality control and biochemical research. Prior to identification by mass spectrometry, proteins are usually pre-separated by electrophoresis. However, current protein staining and de-staining protocols are tedious and time consuming, and therefore prolong the sample preparation time for mass spectrometry. METHODOLOGY AND PRINCIPAL FINDINGS: We developed a 1-minute covalent pre-gel staining protocol for proteins, which does not require de-staining before the mass spectrometry analysis. We investigated the electrophoretic properties of derivatized proteins and peptides and studied their behavior in mass spectrometry. Further, we elucidated the preferred reaction of proteins with Uniblue A and demonstrate the integration of the peptide derivatization into typical informatics tools. CONCLUSIONS AND SIGNIFICANCE: The Uniblue A staining method drastically speeds up the sample preparation for the mass spectrometry based identification of proteins. The application of this chemo-proteomic strategy will be advantageous for routine quality control of proteins and for time-critical tasks in protein analysis.

Abstract: Human diseases can be caused by complex mechanisms involving aberrations in numerous proteins and pathways. With recent advances in genomics, elucidating the molecular basis of disease on a personalized level has become an attainable goal. In many cases, relevant molecular targets will be identified for which approved drugs already exist, and the potential repositioning of these drugs to a new indication can be investigated. Repositioning is an accelerated route for drug discovery because existing drugs have established clinical and pharmacokinetic data. Personalized medicine and repositioning both aim to improve the productivity of current drug discovery pipelines, which expend enormous time and cost to develop new drugs, only to have them fail in clinical trials because of lack of efficacy or toxicity. Here, we discuss the current state of research in these two fields, focusing on recent large-scale efforts to systematically find repositioning candidates and elucidate individual disease mechanisms in cancer. We also discuss scenarios in which personalized drug repositioning could be particularly rewarding, such as for diseases that are rare or have specific mutations, as well as current challenges in this field. With an increasing number of drugs being approved for rare cancer subtypes, personalized medicine and repositioning approaches are poised to significantly alter the way we diagnose diseases, infer treatments and develop new drugs.

Abstract: When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information of the dependency graph. In particular, our approach can efficiently represent both single subgraph topological information and the relation of two subgraphs in the dependency graph. Experimental evaluations showed that our single kernel-based approach can achieve state-of-the-art performance on the publicly available DDI corpus without exploiting multiple kernels or additional domain resources.