3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine.

3D-e-Chem-VM is an open source, freely available Virtual Machine ( http://3d-e-chem.github.io/3D-e-Chem-VM/ ) that integrates cheminformatics and bioinformatics tools for the analysis of protein-ligand interaction data. 3D-e-Chem-VM consists of software libraries, and database and workflow tools that can analyze and combine small molecule and protein structural information in a graphical programming environment. New chemical and biological data analytics tools and workflows have been developed for the efficient exploitation of structural and pharmacological protein-ligand interaction data from proteomewide databases (e.g., ChEMBLdb and PDB), as well as customized information systems focused on, e.g., G protein-coupled receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural cheminformatics research infrastructure compiled in the 3D-e-Chem-VM enables the design of new approaches in virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based protein binding site comparison and bioisosteric replacement for ligand design (KRIPOdb).

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.

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.

Systematic structure-function analysis of androgen receptor Leu701 mutants explains the properties of the prostate cancer mutant L701H.

One mechanism of prostate tumors for escape from androgen ablation therapies is mutation of the androgen receptor (AR). We investigated the unique properties of the AR L701H mutant, which is strongly stimulated by cortisol, by a systematic structure-function analysis. Most amino acid substitutions at position 701 did not affect AR activation by 5alpha-dihydrotestosterone. Further analysis of the AR Leu(701) variants showed that AR L701M and AR L701Q, like AR L701H, had changed ligand responsiveness. AR L701M was strongly activated by progesterone but not by cortisol, whereas the opposite was observed for AR L701Q and AR L701H. Next, we analyzed a panel of structurally related steroids to study which of the OH groups at positions 11beta, 17alpha, and 21, which discriminate cortisol from progesterone, underlie the differential responses to both hormones. The results showed that the 17alpha-OH group was essential for activation of AR L701H and AR L701Q, whereas its absence was important for activation of AR L701M. Modeling indicated a conserved H-bonding network involving the steroidal 17alpha-OH group, His(701) or Gln(701), and the backbone of Ser(778). This network is absent in Leu(701) and in other mutants. A hydrophobic leucine or methionine at position 701 is unfavorable for the 17alpha-OH group. Our results indicate that the specific amino acid residue at position 701, its interaction with the backbone of Ser(778), and the steroidal 17alpha-hydroxyl group of the ligand are all important for the distinct transcriptional responses to progesterone and cortisol of AR mutants, including the prostate cancer mutant L701H.

Discovery and optimisation of a selective non-steroidal glucocorticoid receptor antagonist.

High-throughput screening of 3.87 million compounds delivered a novel series of non-steroidal GR antagonists. Subsequent rounds of optimisation allowed progression from a non-selective ligand with a poor ADMET profile to an orally bioavailable, selective, stable, glucocorticoid receptor antagonist.

The FAIR Guiding Principles for scientific data management and stewardship.

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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.

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.

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.