Data-driven medicinal chemistry in the era of big data.

Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices.

Prednisolone induces the Wnt signalling pathway in 3T3-L1 adipocytes.

Synthetic glucocorticoids are potent anti-inflammatory drugs but show dose-dependent metabolic side effects such as the development of insulin resistance and obesity. The precise mechanisms involved in these glucocorticoid-induced side effects, and especially the participation of adipose tissue in this are not completely understood. We used a combination of transcriptomics, antibody arrays and bioinformatics approaches to characterize prednisolone-induced alterations in gene expression and adipokine secretion, which could underlie metabolic dysfunction in 3T3-L1 adipocytes.

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining.

Background: Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g.

Org 214007-0: a novel non-steroidal selective glucocorticoid receptor modulator with full anti-inflammatory properties and improved therapeutic index.

Glucocorticoids (GCs) such as prednisolone are potent immunosuppressive drugs but suffer from severe adverse effects, including the induction of insulin resistance. Therefore, development of so-called Selective Glucocorticoid Receptor Modulators (SGRM) is highly desirable. Here we describe a non-steroidal Glucocorticoid Receptor (GR)-selective compound (Org 214007-0) with a binding affinity to GR similar to that of prednisolone.

X-ray structure of p38α bound to TAK-715: comparison with three classic inhibitors.

The p38α mitogen-activated protein kinase regulates the synthesis of pro-inflammatory cytokines in response to stimulation by a diverse set of stress signals. Various different chemotypes and clinical candidates that inhibit p38α function have been reported over the years. In this publication, the novel structure of p38α cocrystallized with the clinical candidate TAK-715 is reported.

Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement.

Bioisosteres have been defined as structurally different molecules or substructures that can form comparable intermolecular interactions, and therefore, fragments that bind to similar protein structures exhibit a degree of bioisosterism. We present KRIPO (Key Representation of Interaction in POckets): a new method for quantifying the similarities of binding site subpockets based on pharmacophore fingerprints. The binding site fingerprints have been optimized to improve their performance for both intra- and interprotein family comparisons.

Comparative analysis of pharmacophore screening tools.

The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems.

Integrated project views: decision support platform for drug discovery project teams.

Drug discovery teams continuously have to decide which compounds to progress and which experiments to perform next, but the data required to make informed decisions is often scattered, inaccessible, or inconsistent. In particular, data tend to be stored and represented in a compound-centric or assay-centric manner rather than project-centric as often needed for effective use in drug discovery teams. The Integrated Project Views (IPV) system has been created to fill this gap; it integrates and consolidates data from various sources in a project-oriented manner.

A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design.

We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the β-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models.

X-ray structures of progesterone receptor ligand binding domain in its agonist state reveal differing mechanisms for mixed profiles of 11β-substituted steroids.

We present here the x-ray structures of the progesterone receptor (PR) in complex with two mixed profile PR modulators whose functional activity results from two differing molecular mechanisms. The structure of Asoprisnil bound to the agonist state of PR demonstrates the contribution of the ligand to increasing stability of the agonist conformation of helix-12 via a specific hydrogen-bond network including Glu(723). This interaction is absent when the full antagonist, RU486, binds to PR.

Quantum mechanical polar surface area.

A correlation has been established between the absorbed fraction of training-set molecules after oral administration in humans and the Quantum Mechanical Polar Surface Area (QMPSA). This correlation holds for the QMPSA calculated with structures where carboxyl groups are deprotonated. The correlation of the absorbed fraction and the QMPSA calculated on the neutral gas phase optimized structures is much less pronounced.

Flexible protein-ligand docking using the Fleksy protocol.

Considering protein plasticity is important in accurately predicting the three-dimensional geometry of protein-ligand complexes. Here, we present the first public release of our flexible docking tool Fleksy, which is able to consider both ligand and protein flexibility in the docking process. We describe the workflow and different features of the software and present its performance on two cross-docking benchmark datasets.

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs.

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain.

Structural basis for agonism and antagonism for a set of chemically related progesterone receptor modulators.

The progesterone receptor is able to bind to a large number and variety of ligands that elicit a broad range of transcriptional responses ranging from full agonism to full antagonism and numerous mixed profiles inbetween. We describe here two new progesterone receptor ligand binding domain x-ray structures bound to compounds from a structurally related but functionally divergent series, which show different binding modes corresponding to their agonistic or antagonistic nature. In addition, we present a third progesterone receptor ligand binding domain dimer bound to an agonist in monomer A and an antagonist in monomer B, which display binding modes in agreement with the earlier observation that agonists and antagonists from this series adopt different binding modes.

ss-TEA: Entropy based identification of receptor specific ligand binding residues from a multiple sequence alignment of class A GPCRs.

Background: G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs.

CoPub update: CoPub 5.0 a text mining system to answer biological questions.

In this article, we present CoPub 5.0, a publicly available text mining system, which uses Medline abstracts to calculate robust statistics for keyword co-occurrences. CoPub was initially developed for the analysis of microarray data, but we broadened the scope by implementing new technology and new thesauri.

A molecular informatics view on best practice in multi-parameter compound optimization.

The difference between biologically active molecules and drugs is that the latter balance an array of related and unrelated properties required for administration to patients. Inevitability, during optimization, some of these multiple factors will conflict. Although informatics has a crucial role in addressing the challenges of modern compound optimization, it is arguably still undervalued and underutilized.

GPCRDB: information system for G protein-coupled receptors.

The GPCRDB is a Molecular Class-Specific Information System (MCSIS) that collects, combines, validates and disseminates large amounts of heterogeneous data on G protein-coupled receptors (GPCRs). The GPCRDB contains experimental data on sequences, ligand-binding constants, mutations and oligomers, as well as many different types of computationally derived data such as multiple sequence alignments and homology models. The GPCRDB provides access to the data via a number of different access methods.

Literature mining for the discovery of hidden connections between drugs, genes and diseases.

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates.

Prednisolone-induced differential gene expression in mouse liver carrying wild type or a dimerization-defective glucocorticoid receptor.

Background: Glucocorticoids (GCs) control expression of a large number of genes via binding to the GC receptor (GR). Transcription may be regulated either by binding of the GR dimer to DNA regulatory elements or by protein-protein interactions of GR monomers with other transcription factors. Although the type of regulation for a number of individual target genes is known, the relative contribution of both mechanisms to the regulation of the entire transcriptional program remains elusive.

Integrating gene expression and GO classification for PCA by preclustering.

Background: Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.

Comparison between steroid binding to membrane progesterone receptor alpha (mPRalpha) and to nuclear progesterone receptor: correlation with physicochemical properties assessed by comparative molecular field analysis and identification of mPRalpha-specific agonists.

Recent results showing that the binding characteristics of 33 steroids for human membrane progesterone receptor alpha (hu-mPRalpha) differ from those for the nuclear progesterone receptor (nPR) suggest that hu-mPRalpha-specific agonists can be identified for investigating its physiological functions. The binding affinities of an additional 21 steroids for hu-mPRalpha were determined to explore the structure-activity relationships in more detail and to identify potent, specific mPRalpha agonists. Four synthetic progesterone derivatives with methyl or methylene groups on positions 18 or 19, 18a-methylprogesterone (18-CH(3)P4, Org OE 64-0), 13-ethenyl-18-norprogesterone (18-CH(2)P4, Org 33663-0), 19a-methylprogesterone (19-CH(3)P4, Org OD 13-0) and 10-ethenyl-19-norprogesterone (19-CH(2)P4, Org OD 02-0), showed similar or higher affinities than progesterone for hu-mPRalpha and displayed mPRalpha agonist activities in G-protein and MAP kinase activation assays.

Computational design, synthesis, and evaluation of miniproteins as androgen receptor coactivator mimics.

Insertion of 3 to 4 mutations, based on in silico modelling, in a diverse set of natural miniproteins generates potent androgen receptor (AR) binders and a clear insight into the structure-activity relationship of such coactivator mimics concerning helix length.

BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams.

Background: In many genomics projects, numerous lists containing biological identifiers are produced. Often it is useful to see the overlap between different lists, enabling researchers to quickly observe similarities and differences between the data sets they are analyzing. One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram: a diagram consisting of two or more circles in which each circle corresponds to a data set, and the overlap between the circles corresponds to the overlap between the data sets.