GA4GH Passport standard for digital identity and access permissions.

The Global Alliance for Genomics and Health (GA4GH) supports international standards that enable a federated data sharing model for the research community while respecting data security, ethical and regulatory frameworks, and data authorization and access processes for sensitive data. The GA4GH Passport standard (Passport) defines a machine-readable digital identity that conveys roles and data access permissions (called "visas") for individual users. Visas are issued by data stewards, including data access committees (DACs) working with public databases, the entities responsible for the quality, integrity, and access arrangements for the datasets in the management of human biomedical data. Passports streamline management of data access rights across data systems by using visas that present a data user's digital identity and permissions across organizations, tools, environments, and services. We describe real-world implementations of the GA4GH Passport standard in use cases from ELIXIR Europe, National Institutes of Health, and the Autism Sharing Initiative. These implementations demonstrate that the Passport standard has provided transparent mechanisms for establishing permissions and authorizing data access across platforms.

International federation of genomic medicine databases using GA4GH standards.

We promote a shared vision and guide for how and when to federate genomic and health-related data sharing, enabling connections and insights across independent, secure databases. The GA4GH encourages a federated approach wherein data providers have the mandate and resources to share, but where data cannot move for legal or technical reasons. We recommend a federated approach to connect national genomics initiatives into a global network and precision medicine resource.

Computational and functional analysis of the androgen receptor antagonist atraric acid and its derivatives.

Androgen receptor (AR) antagonists are important compounds for the treatment of prostate cancer (PCa). The atraric acid (AA), a natural compound, binds to the AR and acts as a specific AR antagonist. Interestingly, AA represents a novel chemical platform that could serve as a potential basis for new AR antagonists. Therefore, one objective of this study was to analyze the chemical/structural requirements for AR antagonism and to obtain predictions of where and how AA binds to the AR. Further, this study describes the chemical synthesis of 12 AA derivatives and their analysis using a combination of computational and functional assays. Functional analysis of AA derivatives indicated that none activated the AR. Both the para-hydroxyl group and the benzene ortho- and the meta-methyl groups of AA appeared to be essential to antagonize androgen-activated AR activity. Furthermore, extension of the hydrophobic side chain of AA led to slightly stronger AR antagonism. In silico data suggest that modifications to the basic AA structure change the hydrogen-bonding network with the AR ligand binding domain (LBD), so that the para-hydroxyl group of AA forms a hydrogen bond with the LBD, confirming the functional importance of this group for AR antagonism. Moreover, in silico modeling also suggested that the ortho- and meta- methyl groups of AA interact with hydrophobic residues of the ligand pocket of AR, which might explain their functional importance for antagonism. Thus, these studies identify the chemical groups of AA that play key roles in allowing the AA-based chemical platform to act as an AR antagonist.

Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers.

Funding bodies are increasingly recognizing the need to provide graduates and researchers with access to short intensive courses in a variety of disciplines, in order both to improve the general skills base and to provide solid foundations on which researchers may build their careers. In response to the development of 'high-throughput biology', the need for training in the field of bioinformatics, in particular, is seeing a resurgence: it has been defined as a key priority by many Institutions and research programmes and is now an important component of many grant proposals. Nevertheless, when it comes to planning and preparing to meet such training needs, tension arises between the reward structures that predominate in the scientific community which compel individuals to publish or perish, and the time that must be devoted to the design, delivery and maintenance of high-quality training materials. Conversely, there is much relevant teaching material and training expertise available worldwide that, were it properly organized, could be exploited by anyone who needs to provide training or needs to set up a new course. To do this, however, the materials would have to be centralized in a database and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review it, respectively, to similar initiatives and collections.

Structural basis for computational screening of non-steroidal androgen receptor ligands.

IMPORTANCE OF THE FIELD: Deep structural and chemical understanding of the protein target and computational methods for detection of receptor-selective ligands are important for the early drug discovery in the steroid receptor field. AREAS COVERED IN THIS REVIEW: This review focuses on the use of currently available structural information of the androgen receptor (AR) and known AR ligands to make computational strategies for the discovery of AR ligands in order to offer new chemical platforms for drug development. WHAT THE READER WILL GAIN: AR is a challenging target for drug discovery and modeling even if there is a wealth of experimental data available. First, only the active structure of AR is currently known, which hampers the design of AR antagonists. Second, the structural similarity between the ligand-binding sites of AR and its mutated forms and closely related steroid receptors (SRs) such as progesterone receptors presents challenges for the development of drugs with receptor-selective function. TAKE HOME MESSAGE: Research indicates that a very small chemical change in the structure of a non-steroidal ligand can cause a complete change in its activity. One source of this effect arises from binding to similar binding sites in related SRs and other proteins in the signaling pathway. Currently, computational methods are not able to predict the subtle differences between AR ligand activities but modeling does offer the possibility of generating new lead structures that might have the desired properties.

Computationally identified novel diphenyl- and phenylpyridine androgen receptor antagonist structures.

We have identified and profiled a set of androgen receptor (AR) binding compounds representing two nonsteroidal scaffolds from a public chemical database supplied by Asinex with virtual screening procedure incorporating our recently published 3D QSAR model of AR ligands. The diphenyl- and phenylpyridine-based compounds act as antagonists in wild-type AR in CV1 cells and also retain this antagonistic character in CV1 cells expressing T877A mutant receptor. This mutation is frequently associated with prostate cancer. Two of the compounds repress the androgen-dependent cell growth of LNCaP prostate cancer cells expressing the T877A AR mutant. Molecular modeling of the observed in vitro antagonism with induced fit docking suggests that W741 and M895 could be mechanistically involved in the initiation of the antagonism. The results indicate finding of nonsteroidal AR antagonist compounds from a public chemical database with computational methods. Compounds could serve as a novel platform to develop more potent AR antagonists with inhibitory activity in both wild-type and T877A mutant AR.

Docking and three-dimensional quantitative structure-activity relationship (3D QSAR) analyses of nonsteroidal progesterone receptor ligands.

We report a docking and comparative molecular similarity indices analysis (CoMSIA) study of progesterone receptor (PR) ligands with an emphasis on nonsteroids including tanaproget. The ligand alignment generation, a critical part of model building, comprised two stages. First, thorough conformational sampling of docking poses within the PR binding pocket was made with the program GOLD. Second, a strategy to select representative poses for CoMSIA was developed utilizing the FlexX scoring function. After manual replacement of five poses where this approach had problems, a significant correlation (r(2) = 0.878) between the experimental affinities and electrostatic, hydrophobic, and hydrogen bond donor properties of the aligned ligands was found. Extensive model validation was made using random-group cross-validations, external test set predictions (r(pred)(2) = 0.833), and consistency check between the CoMSIA model and the PR binding site structure. Robustness, predictive ability, and automated alignment generation make the model a potential tool for virtual screening.

SOMA--workflow for small molecule property calculations on a multiplatform computing grid.

We present the concept of the SOMA workflow developed at the Finnish IT Center for Science CSC. The SOMA workflow unites multiplatform UNIX/LINUX computing resources and third-party software for calculating molecular structure and properties. The presented workflow components consist of the computing program XML descriptions, the core workflow program Grape, the toolkit for parsing program input and output, and the extranet interface. The program Grape and the developed XML descriptions of scientific programs allow researchers to link molecular modeling software into highly sophisticated computational workflows. SOMA collects the calculated data produced by the workflow and stores the computed information in the Chemical Markup Language (CML) format. The extranet interface is used for user authentication, building of the program interfaces and the workflows, and for sorting, filtering, and visualizing the results.

BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. 1. Alignment and virtual screening applications.

We have developed a fast grid-based algorithm, BRUTUS, for rigid-body molecular superposition and similarity searching. BRUTUS aligns molecules using field information derived from charge distributions and van der Waals shapes of the compounds. Molecules can have similar biological properties if their charge distributions and shapes are similar, even though they have different chemical structures; that is, BRUTUS can identify compounds possessing similar properties, regardless of their structures. In this paper, we present two applications of BRUTUS. First, BRUTUS was used to superimpose five sets of inhibitors. Second, two sets of known inhibitors were searched from a database, and the results were analyzed using self-organizing maps. We demonstrate that BRUTUS is successful in superimposing compounds using molecular fields and, importantly, is fast and accurate enough for virtual screening of chemical databases using a standard personal computer. This fast and efficient molecular-field-based algorithm is applicable for virtual screening of structurally diverse, active molecules.

Three-dimensional structure-activity relationships of nonsteroidal ligands in complex with androgen receptor ligand-binding domain.

We studied the three-dimensional quantitative structure-activity relationships (3D QSAR) of 70 structurally and functionally diverse androgen receptor (AR) binding compounds using the comparative molecular similarity indices analysis (CoMSIA) method. The compound set contained 67 nonsteroidal analogues of flutamide, nilutamide, and bicalutamide whose binding mode to AR was unknown. Docking was used to identify the preferred binding modes for the nonsteroidal compounds within the AR ligand-binding pocket (LBP) and to generate the ligand alignment for the 3D QSAR analysis. The alignment produced a statistically significant and predictive model, validated by random group cross-validation and external test sets (q(2)(LOO) = 0.656, SDEP = 0.576, r(2) = 0.911, SEE = 0.293; q(2)(10) = 0.612, q(2)(5) = 0.571; pred-r(2) = 0.800). Additional model validation comes from the CoMSIA maps that were interpreted with respect to the LBP structure. The model takes into account and links the AR LBP structure, docked ligand structures, and the experimental binding activities. The results provide valuable information on intermolecular interactions between nonsteroidal ligands and the AR LBP.

An in silico approach to discovering novel inhibitors of human sirtuin type 2.

Type 2 human sirtuin (SIRT2) is a NAD(+)-dependent cytoplasmic protein that is colocalized with HDAC6 on microtubules. SIRT2 has been shown to deacetylate alpha-tubulin and to control mitotic exit in the cell cycle. To date, some small molecular inhibitors of SIRT2 have been identified; however, more inhibitors are still needed to improve the understanding of SIRT2 biological function and to discover its possible therapeutic indications. In this paper, an in silico identification procedure is described for discovering novel SIRT2 inhibitors. Molecular modeling and virtual screening were utilized to find potential compounds, which were then subjected to experimental tests for their SIRT2 inhibitory activity. Five of the 15 compounds tested in vitro showed inhibitory activity toward SIRT2, yielding a hit ratio of 33% in a micromolar level and thus demonstrating the usefulness of this procedure in finding new bioactive compounds. Two of the five compounds yielded in vitro IC(50) values of 56.7 and 74.3 microM, and these can be considered as novel inhibitors of SIRT2. On the basis of our results, a phenol moiety on the active compound is suggested to be important for SIRT2 inhibitory activity. This phenol group, together with a hydrophobic moiety and hydrogen-bonding features, is suggested to form an active SIRT2 pharmacophore.

Comparing the quality and predictiveness between 3D QSAR models obtained from manual and automated alignment.

A set of 113 flexible cyclic urea inhibitors of human immunodeficiency virus protease (HIV-1 PR) was used to compare the quality and predictive power of CoMFA and CoMSIA models for manually or automatically aligned inhibitor set. Inhibitors that were aligned automatically with molecular docking were in agreement with information obtained from existing X-ray structures. Both alignment methods produced statistically significant CoMFA and CoMSIA models, with the best q(2) value being 0.649 and the best predictive r(2) being 0.754. The manual alignment gave statistically higher values, whereas the automated alignment gave more robust models for predicting the activities of an external inhibitor set. Both models utilized similar amino acids in the HIV-1 PR active site, supporting the idea that hydrogen bonds form between an inhibitor and the backbone carbonyl oxygens of Gly48 and Gly48' and also the backbone NH group of Asp30, Gly48, Asp29', and Gly48' of the enzyme. These results suggest that an automated inhibitor alignment can yield predictive 3D QSAR models that are well comparable to manual methods. Thus, an automated alignment method in creating 3D QSAR models is encouragable when a well-characterized structure of the target protein is available.

A structure-activity relationship study of catechol-O-methyltransferase inhibitors combining molecular docking and 3D QSAR methods.

A panel of 92 catechol-O-methyltransferase (COMT) inhibitors was used to examine the molecular interactions affecting their biological activity. COMT inhibitors are used as therapeutic agents in the treatment of Parkinson's disease, but there are limitations in the currently marketed compounds due to adverse side effects. This study combined molecular docking methods with three-dimensional structure-activity relationships (3D QSAR) to analyse possible interactions between COMT and its inhibitors, and to incite the design of new inhibitors. Comparative molecular field analysis (CoMFA) and GRID/GOLPE models were made by using bioactive conformations from docking experiments, which yielded q2 values of 0.594 and 0.636, respectively. The docking results, the COMT X-ray structure, and the 3D QSAR models are in agreement with each other. The models suggest that an interaction between the inhibitor's catechol oxygens and the Mg2+ ion in the COMT active site is important. Both hydrogen bonding with Lys144, Asn170 and Glu199, and hydrophobic contacts with Trp38, Pro174 and Leu198 influence inhibitor binding. Docking suggests that a large R1 substituent of the catechol ring can form hydrophobic contacts with side chains of Val173, Leu198, Met201 and Val203 on the COMT surface. Our models propose that increasing steric volume of e.g. the diethylamine tail of entacapone is favourable for COMT inhibitory activity.