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. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.

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. Several pathways, including cytokine signalling, Akt signalling, and Wnt signalling were found to be regulated at multiple levels, showing that these processes are targeted by prednisolone. These results suggest that mechanisms by which prednisolone induce insulin resistance include dysregulation of wnt signalling and immune response processes. These pathways may provide interesting targets for the development of improved glucocorticoids.

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. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids.Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects. RESULTS: We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes.With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6-phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR. CONCLUSIONS: With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks.

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. Structural modelling of the GR-Org 214007-0 binding site shows disturbance of the loop between helix 11 and helix 12 of GR, confirmed by partial recruitment of the TIF2-3 peptide. Using various cell lines and primary human cells, we show here that Org 214007-0 acts as a partial GC agonist, since it repressed inflammatory genes and was less effective in induction of metabolic genes. More importantly, in vivo studies in mice indicated that Org 214007-0 retained full efficacy in acute inflammation models as well as in a chronic collagen-induced arthritis (CIA) model. Gene expression profiling of muscle tissue derived from arthritic mice showed a partial activity of Org 214007-0 at an equi-efficacious dosage of prednisolone, with an increased ratio in repression versus induction of genes. Finally, in mice Org 214007-0 did not induce elevated fasting glucose nor the shift in glucose/glycogen balance in the liver seen with an equi-efficacious dose of prednisolone. All together, our data demonstrate that Org 214007-0 is a novel SGRMs with an improved therapeutic index compared to prednisolone. This class of SGRMs can contribute to effective anti-inflammatory therapy with a lower risk for metabolic side effects.

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. Owing to the impact of crystallization conditions on the conformation of protein kinases (and in particular p38α), the structures of complexes of p38α with SB-203580, SCIO-469 and VX-745 have also been determined to enable in-depth comparison of ligand-induced protein conformations. The impact of experimental conditions on p38α-inhibitor complex structures, most importantly soaking versus cocrystallization, is discussed. Analysis of the structures and quantification of the protein-ligand interactions couples ligand-induced protein conformations to the number of interactions and to inhibitor selectivity against the human kinome. This shows that for the design of novel kinase inhibitors, selectivity is best obtained through maximization of the number of interactions throughout the ATP pocket and the exploitation of specific features in the active site.

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. A range of attributes of the fingerprints was considered in the optimization, including the placement of pharmacophore features, whether or not the fingerprints are fuzzified, and the resolution and complexity of the pharmacophore fingerprints (2-, 3-, and 4-point fingerprints). Fuzzy 3-point pharmacophore fingerprints were found to represent the optimal balance between computational resource requirements and the identification of potential replacements. The complete PDB was converted into a database comprising almost 300,000 optimized fingerprints of local binding sites together with their associated ligand fragments. The value of the approach is demonstrated by application to two crystal structures from the Protein Data Bank: (1) a MAP kinase P38 structure in complex with a pyridinylimidazole inhibitor (1A9U) and (2) a complex of thrombin with melagatran (1K22). Potentially valuable bioisosteric replacements for all subpockets of the two studied protein are identified.

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. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.

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. Its automatic gathering and updating of project data not only ensures that the information is comprehensive and available on a timely basis, but also improves the data consistency. Due to the lack of suitable off-the-shelf solutions, we were prompted to develop custom functionality and algorithms geared specifically to our drug discovery decision making process. In 10 years of usage, the resulting IPV application has become very well-accepted and appreciated, which is perhaps best evidenced by the observation that standalone Excel spreadsheets are largely eliminated from project team meetings.

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 ß-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. Surprisingly, the phosphodiesterase 5 (PDE5) inhibitor sildenafil was found to possess submicromolar affinity for AA2AR. Overall, this is one of the first published prospective chemogenomics studies that demonstrate the identification of novel cross-pharmacology between unrelated protein targets. The lessons learned from this study can be used to guide future virtual ligand design efforts.

X-ray structures of progesterone receptor ligand binding domain in its agonist state reveal differing mechanisms for mixed profiles of 11ß-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. Combined with a previously reported structure of Asoprisnil bound to the antagonist state of the receptor, this structure extends our understanding of the complex molecular interactions underlying the mixed agonist/antagonist profile of the compound. In addition, we present the structure of PR in its agonist conformation bound to the mixed profile compound Org3H whose reduced antagonistic activity and increased agonistic activity compared with reference antagonists is due to an induced fit around Trp(755), resulting in a decreased steric clash with Met(909) but inducing a new internal clash with Val(912) in helix-12. This structure also explains the previously published observation that 16α attachments to RU486 analogs induce mixed profiles by altering the binding of 11ß substituents. Together these structures further our understanding of the steric and electrostatic factors that contribute to the function of steroid receptor modulators, providing valuable insight for future compound design.

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. RESULTS: Here we present 1) ss-TEA, a method to identify specific ligand binding residue positions for any receptor, predicated on high quality sequence information. 2) The largest MSA of class A non olfactory GPCRs in the public domain consisting of 13324 sequences covering most of the species homologues of the human set of GPCRs. A set of ligand binding residue positions extracted from literature of 10 different receptors shows that our method has the best ligand binding residue prediction for 9 of these 10 receptors compared to another state-of-the-art method. CONCLUSIONS: The combination of the large multi species alignment and the newly introduced residue selection method ss-TEA can be used to rapidly identify subfamily specific ligand binding residues. This approach can aid the design of site directed mutagenesis experiments, explain receptor function and improve modelling. The method is also available online via GPCRDB at http://www.gpcr.org/7tm/.

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. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.

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.

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. In CoPub 5.0, we integrated existing CoPub technology with new features, and provided a new advanced interface, which can be used to answer a variety of biological questions. CoPub 5.0 allows searching for keywords of interest and its relations to curated thesauri and provides highlighting and sorting mechanisms, using its statistics, to retrieve the most important abstracts in which the terms co-occur. It also provides a way to search for indirect relations between genes, drugs, pathways and diseases, following an ABC principle, in which A and C have no direct connection but are connected via shared B intermediates. With CoPub 5.0, it is possible to create, annotate and analyze networks using the layout and highlight options of Cytoscape web, allowing for literature based systems biology. Finally, operations of the CoPub 5.0 Web service enable to implement the CoPub technology in bioinformatics workflows. CoPub 5.0 can be accessed through the CoPub portal http://www.copub.org.

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. We present here some of the basic requirements of multi-parameter drug design, the crucial role of informatics and examples of favorable practice. The most crucial of these best practices are the need for informaticians to align their technologies and insights directly to discovery projects and for all scientists in drug discovery to become more proficient in the use of in silico methods.

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. It offers visualization and analysis tools, and a number of query systems. The data is updated automatically on a monthly basis. The GPCRDB can be found online at http://www.gpcr.org/7tm/.

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. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.

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. To study the importance of GR dimerization in the regulation of gene expression, we performed gene expression profiling of livers of prednisolone-treated wild type (WT) and mice that have lost the ability to form GR dimers (GRdim). RESULTS: The GR target genes identified in WT mice were predominantly related to glucose metabolism, the cell cycle, apoptosis and inflammation. In GRdim mice, the level of prednisolone-induced gene expression was significantly reduced compared to WT, but not completely absent. Interestingly, for a set of genes, involved in cell cycle and apoptosis processes and strongly related to Foxo3a and p53, induction by prednisolone was completely abolished in GRdim mice. In contrast, glucose metabolism-related genes were still modestly upregulated in GRdim mice upon prednisolone treatment. Finally, we identified several novel GC-inducible genes from which Fam107a, a putative histone acetyltransferase complex interacting protein, was most strongly dependent on GR dimerization. CONCLUSIONS: This study on prednisolone-induced effects in livers of WT and GRdim mice identified a number of interesting candidate genes and pathways regulated by GR dimers and sheds new light onto the complex transcriptional regulation of liver function by GCs.

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.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering. RESULTS: Two microarray datasets are analyzed. First, a selection of genes from a well-known Saccharomyces cerevisiae dataset is used. The GO class "cell wall organization and biogenesis" is shown as a specific example. After preclustering, this term can be associated with different phases in the cell cycle, where it could not be associated with a specific phase previously. Second, a dataset of differentiation of human Mesenchymal Stem Cells (MSC) into osteoblasts is used. For this dataset results are shown in which the GO term "skeletal development" is a specific example of a heterogeneous GO class for which better associations can be made after preclustering. The Intra Cluster Correlation (ICC), a measure of cluster tightness, is applied to identify relevant clusters. CONCLUSIONS: We show that this method leads to an improved interpretability of results in Principal Component Analysis.