The BioAssay network and its implications to future therapeutic discovery

Background: Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem. Results: In this paper we construct a network from bioassay data in PubChem, apply network biology concepts to characterize this bioassay network, integrate information from multiple biological databases (e.g. DrugBank, OMIM, and UniHI), and systematically analyze the potential of bioassay targets being new drug targets in the context of complex biological networks. We propose a model to quantitatively prioritize this druggability of bioassay targets, and literature evidence was found to confirm our prioritization of bioassay targets at a roughly 70% accuracy. Conclusions: Our analysis provide some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery

The BioAssay network and its implications to future therapeutic discovery

Background: Despite intense investment growth and technology development, there is an observed bottleneck in drug discovery and development over the past decade. NIH started the Molecular Libraries Initiative (MLI) in 2003 to enlarge the pool for potential drug targets, especially from the “undruggable” part of human genome, and potential drug candidates from much broader types of drug-like small molecules. All results are being made publicly available in a web portal called PubChem. Results: In this paper we construct a network from bioassay data in PubChem, apply network biology concepts to characterize this bioassay network, integrate information from multiple biological databases (e.g. DrugBank, OMIM, and UniHI), and systematically analyze the potential of bioassay targets being new drug targets in the context of complex biological networks. We propose a model to quantitatively prioritize this druggability of bioassay targets, and literature evidence was found to confirm our prioritization of bioassay targets at a roughly 70% accuracy. Conclusions: Our analysis provide some measures of the value of the MLI data as a resource for both basic chemical biology research and future therapeutic discovery

A filter-based feature selection approach for identifying potential biomarkers for lung cancer

Background: Lung cancer is the leading cause of death from cancer in the world and its treatment is dependant on the type and stage of cancer detected in the patient. Molecular biomarkers that can characterize the cancer phenotype are thus a key tool in planning a therapeutic response. A common protocol for identifying such biomarkers is to employ genomic microarray analysis to find genes that show differential expression according to disease state or type. Data-mining techniques such as feature selection are often used to isolate, from among a large manifold of genes with differential expression, those specific genes whose differential expression patterns are of optimal value in phenotypic differentiation. One such technique, Biomarker Identifier (BMI), has been developed to identify features with the ability to distinguish between two data groups of interest, which is thus highly applicable for such studies. Results: Microarray data with validated genes was used to evaluate the utility of BMI in identifying markers for lung cancer. This data set contains a set of 129 gene expression profiles from large-airway epithelial cells (60 samples from smokers with lung cancer and 69 from smokers without lung cancer) and 7 genes from this data have been confirmed to be differentially expressed by quantitative PCR. Using this data set, BMI was compared with various well-known feature selection methods and was found to be more successful than other methods in finding useful genes to classify cancerous samples. Also it is evident that genes selected by BMI (given the same number of genes and classification algorithms) showed better discriminative power than those from the original study. After pathway analysis on the selected genes by BMI, we have been able to correlate the selected genes with well-known cancer-related pathways. Conclusions: Our results show that BMI can be used to analyze microarray data and to find useful genes for classifying samples. Pathway analysis suggests that BMI is successful in identifying biomarker-quality cancer-related genes from the data

Reagent based DOS: A "Click, Click, Cyclize" strategy to probe chemical space

The synthesis of small organic molecules as probes for discovering new therapeutic agents has been an important aspect of chemical-biology. Herein we report a reagent-based, diversity-oriented synthetic (DOS) strategy to probe chemical and biological space via a “Click, Click, Cyclize” protocol. In this DOS approach, three sulfonamide linchpins underwent cyclization protocols with a variety of reagents to yield a collection of structurally diverse S-heterocycles. In silico analysis is utilized to evaluate the diversity of the compound collection against chemical space (PC analysis), shape space (PMI) and polar surface area (PSA) calculations

Identification, Ki determination and CoMFA analysis of nuclear receptor ligands as competitive inhibitors of OATP1B1-mediated estradiol-17β-glucuronide transport

Evidence shows that drug-drug interactions can occur at the level of drug transporters such as the organic anion transporting polypeptides (OATPs), a group of membrane solute carriers that mediate the sodium-independent transport of a wide range of amphipathic organic compounds. The polyspecific OATP1B1 is exclusively expressed at the basolateral membrane of hepatocytes and mediates uptake of amphipathic organic compounds from blood into hepatocytes. Nuclear receptors are ligand-activated transcription factors that play an important role in xenobiotic disposition and human diseases. Quite a few nuclear receptor ligands interact with transport proteins. A high-resolution three-dimensional structure is critical to understand the polyspecificity of OATP1B1 to predict and prevent adverse drug-drug interactions. Unfortunately there are no crystal structures of OATPs/Oatps available to date. Therefore, in this study we attempted to elucidate the characteristics of the substrate binding site of OATP1B1 based on small molecules interacting with it. First, we identified inhibitors of the OATP1B1 model substrate estradiol-17β-glucuronide from about forty nuclear receptor ligands. Among them, GW1929, paclitaxel and troglitazone were strong inhibitors, while 5α-androstane, 5α-androstane-3β, 17β-diol-17-hexahydrobenzoate and estradiol-3-benzoate were weak inhibitors. Then, we selected 25 compounds and performed inhibition kinetic studies to identify competitive inhibitors and determine their Ki values which ranged from submicromolar to submillimolar. Finally, we performed CoMFA analysis on the identified competitive inhibitors. The CoMFA results indicate that the substrate binding site of OATP1B1 consists of a large hydrophobic middle part with basic residues at both ends that could be very important for substrate binding

1,3-Allylic Strain as a Strategic Diversification Element For Constructing Libraries of Substituted 2-Arylpiperidines

Flipping diversity—Minimization of 1,3-allylic strain is a recurring element in the design of a stereochemically- and spatially-diverse collection of 2-arylpiperidines. Here, stereochemicallydiverse scaffolding is first constructed using A1,3 strain to guide the regioselective addition of nucleophiles, which serve as handles for further substitution. N-substitution with alkyl and acyl substituents again leverages A1,3 strain to direct each stereoisomer to two different conformer populations, doubling the number of library member

GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics

Graph data mining is an active research area. Graphs are general modeling tools to organize information from heterogeneous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community. In this paper, we demonstrate a novel technique called graph pattern diffusion (GPD) kernel. Our idea is to leverage existing frequent pattern discovery methods and to explore the application of kernel classifier (e.g., support vector machine) in building highly accurate graph classification. In our method, we first identify all frequent patterns from a graph database. We then map subgraphs to graphs in the graph database and use a process we call “pattern diffusion” to label nodes in the graphs. Finally, we designed a graph alignment algorithm to compute the inner product of two graphs. We have tested our algorithm using a number of chemical structure data. The experimental results demonstrate that our method is significantly better than competing methods such as those kernel functions based on paths, cycles, and subgraphs

Glycosylation Effects on FSH-FSHR Interaction Dynamics: A Case Study of Different FSH Glycoforms by Molecular Dynamics Simulations

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedicationThe gonadotropin known as follicle-stimulating hormone (FSH) plays a key role in regulating reproductive processes. Physiologically active FSH is a glycoprotein that can accommodate glycans on up to four asparagine residues, including two sites in the FSH alpha subunit that are critical for biochemical function, plus two sites in the beta subunit, whose differential glycosylation states appear to correspond to physiologically distinct functions. Some degree of FSH beta hypo-glycosylation seems to confer advantages toward reproductive fertility of childbearing females. In order to identify possible mechanistic underpinnings for this physiological difference we have pursued computationally intensive molecular dynamics simulations on complexes between the high affinity site of the gonadal FSH receptor (FSHR) and several FSH glycoforms including fully-glycosylated (FSH24), hypo-glycosylated (e.g., FSH15), and completely deglycosylated FSH (dgFSH). These simulations suggest that deviations in FSH/FSHR binding profile as a function of glycosylation state are modest when FSH is adorned with only small glycans, such as single N-acetylglucosamine residues. However, substantial qualitative differences emerge between FSH15 and FSH24 when FSH is decorated with a much larger, tetra-antennary glycan. Specifically, the FSHR complex with hypo-glycosylated FSH15 is observed to undergo a significant conformational shift after 5-10 ns of simulation, indicating that FSH15 has greater conformational flexibility than FSH24 which may explain the more favorable FSH15 kinetic profile. FSH15 also exhibits a stronger binding free energy, due in large part to formation of closer and more persistent salt-bridges with FSHR.This research was supported by National Institute of Health Grant P01 AG-029531 to GRB. LiS Consulting provided support in the form of a salary for GHL, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the 'author contributions' section

Cloning and characterization of feline islet glucokinase

Background: Glucokinase (GK) is a metabolic enzyme encoded by the GCK gene and expressed in glucose-sensitive tissues, principally pancreatic islets cell and hepatocytes. The GK protein acts in pancreatic islets as a “glucose sensor” that couples fluctuations in the blood glucose concentration to changes in cellular function and insulin secretion. GCK and GK have proposed importance in the development and progression of diabetes mellitus and are potential therapeutic targets for diabetes treatment. The study was undertaken to determine the nucleotide sequence of feline pancreatic GK cDNA, predict the amino acid sequence and structure of the feline GK protein, and perform comparative bioinformatic analysis of feline cDNA and protein. Routine PCR techniques were used with cDNA from feline pancreas. Clones were assembled to obtain the full length cDNA. Protein prediction and modeling were performed using bioinformatic tools. Results: Full-length feline pancreatic GK cDNA contains a 1398 nucleotide coding sequence with high identity to other pancreatic GK cDNAs. The deduced 465 amino acid feline protein has 15 amino acid substitutions not found in other mammalian GK proteins but maintains high structural homology with human GK. Feline pancreatic GK is highly conserved at nucleotide and protein levels. Residues crucial for substrate binding and catalysis are completely conserved in the feline protein. Conclusion: Molecular analysis predicts that feline pancreatic GK functions similarly to other mammalian GK proteins