The Nuclear Receptor Resource: a growing family

Unraveling allosteric landscapes of allosterome with ASD

Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.

iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families

Nuclear receptor proteins (NRPs) perform a vital role in regulating gene expression. With the rapidity growth of NRPs in post-genomic era, it is highly recommendable to identify NRPs and their sub-families accurately from their primary sequences. Several conventional methods have been used for discrimination of NRPs and their sub-families, but did not achieve considerable results. In a sequel, a two-level new computational model "iNR-2 L" is developed. Two discrete methods namely: Dipeptide Composition and Tripeptide Composition were used to formulate NRPs sequences. Further, both the descriptor spaces were merged to construct hybrid space. Furthermore, feature selection technique minimum redundancy and maximum relevance was employed in order to select salient features as well as reduce the noise and redundancy. The experiential outcomes exhibited that the proposed model iNR-2 L achieved outstanding results. It is anticipated that the proposed computational model might be a practical and effective tool for academia and research community.

A review of the new HGNC gene family resource

The HUGO Gene Nomenclature Committee (HGNC) approves unique gene symbols and names for human loci. As well as naming genomic loci, we manually curate genes into family sets based on shared characteristics such as function, homology or phenotype. Each HGNC gene family has its own dedicated gene family report on our website, www.genenames.org. We have recently redesigned these reports to support the visualisation and browsing of complex relationships between families and to provide extra curated information such as family descriptions, protein domain graphics and gene family aliases. Here, we review how our gene families are curated and explain how to view, search and download the gene family data.

NURBS: a database of experimental and predicted nuclear receptor binding sites of mouse

SUMMARY

Nuclear receptors (NRs) are a class of transcription factors playing important roles in various biological processes. An NR often impacts numerous genes and different NRs share overlapped target networks. To fulfil the need for a database incorporating binding sites of different NRs at various conditions for easy comparison and visualization to improve our understanding of NR binding mechanisms, we have developed NURBS, a database for experimental and predicted nuclear receptor binding sites of mouse (NURBS). NURBS currently contains binding sites across the whole-mouse genome of 8 NRs identified in 40 chromatin immunoprecipitation with massively parallel DNA sequencing experiments. All datasets are processed using a widely used procedure and same statistical criteria to ensure the binding sites derived from different datasets are comparable. NURBS also provides predicted binding sites using NR-HMM, a Hidden Markov Model (HMM) model.

AVAILABILITY

The GBrowse-based user interface of NURBS is freely accessible at http://shark.abl.ku.edu/nurbs/. NR-HMM and all results can be downloaded for free at the website.

CONTACT

jwfang@ku.edu

Receptor databases and computational websites for ligand binding

Ligand binding to receptors is a key step in the regulation of cellular function by neurotransmitters, hormones, and many drugs. Not surprisingly then, genome projects have found that families of receptor genes form the largest groups of functional genes in mammalian genomes. A large body of experimental data have thus been generated on receptor-ligand interactions, and in turn, numerous computational tools for the in silico prediction of receptor-ligand interactions have been developed. Websites containing ligand binding data and tools to assess and manipulate such data are available in the public domain. Such Websites provide a resource for experimentalists studying receptor binding and for scientists interested in utilizing large data sets for other purposes, which include modeling structure-function relationships, defining patterns of interactions of drugs with different receptors, and computational comparisons among receptors. The Websites include databases of receptor protein and nucleotide sequences for particular classes of receptors (such as G-protein-coupled receptors and nuclear receptors) and of experimental results from receptor-ligand binding assays, as well as computational tools for modeling the interactions between ligands and receptors and predicting the function of orphan receptors. In this chapter, we provide information and Uniform Resource Locators (URLs) for Websites that facilitate computational and experimental studies of receptor-ligand interactions. This list will be periodically updated at https://sites.google.com/site/receptorligandbinding/.

Discovery-driven research and bioinformatics in nuclear receptor and coregulator signaling

Nuclear receptors (NRs) are a superfamily of ligand-regulated transcription factors that interact with coregulators and other transcription factors to direct tissue-specific programs of gene expression. Recent years have witnessed a rapid acceleration of the output of high-content data platforms in this field, generating discovery-driven datasets that have collectively described: the organization of the NR superfamily (phylogenomics); the expression patterns of NRs, coregulators and their target genes (transcriptomics); ligand- and tissue-specific functional NR and coregulator sites in DNA (cistromics); the organization of nuclear receptors and coregulators into higher order complexes (proteomics); and their downstream effects on homeostasis and metabolism (metabolomics). Significant bioinformatics challenges lie ahead both in the integration of this information into meaningful models of NR and coregulator biology, as well as in the archiving and communication of datasets to the global nuclear receptor signaling community. While holding great promise for the field, the ascendancy of discovery-driven research in this field brings with it a collective responsibility for researchers, publishers and funding agencies alike to ensure the effective archiving and management of these data. This review will discuss factors lying behind the increasing impact of discovery-driven research, examples of high-content datasets and their bioinformatic analysis, as well as a summary of currently curated web resources in this field. This article is part of a Special Issue entitled: Translating nuclear receptors from health to disease.

Prediction of nuclear receptors with optimal pseudo amino acid composition

Nuclear receptors are involved in multiple cellular signaling pathways that affect and regulate processes such as organ development and maintenance, ion transport, homeostasis, and apoptosis. In this article, an optimal pseudo amino acid composition based on physicochemical characters of amino acids is suggested to represent proteins for predicting the subfamilies of nuclear receptors. Six physicochemical characters of amino acids were adopted to generate the protein sequence features via web server PseAAC. The optimal values of the rank of correlation factor and the weighting factor about PseAAC were determined to get the appropriate descriptor of proteins that leads to the best performance. A nonredundant dataset of nuclear receptors in four subfamilies is constructed to evaluate the method using support vector machines. An overall accuracy of 99.6% was achieved in the fivefold cross-validation test as well as the jackknife test, and an overall accuracy of 98.4% was reached in a blind dataset test. The performance is very competitive with that of some previous methods.

NRMD: Nuclear Receptor Mutation Database

The NRMD is a database for nuclear receptor mutation information. It includes mutation information from SWISS-PROT/TrEMBL, several web-based mutation data resources, and data extracted from the literature in a fully automatic manner. Because it is also possible to add mutations manually, a hundred mutations were added for completeness. At present, the NRMD contains information about 893 mutations in 54 nuclear receptors. A common numbering scheme for all nuclear receptors eases the use of the information for many kinds of studies. The NRMD is freely available to academia and industry as a stand-alone version at: www.receptors.org/NR/.

Loss of androgen receptor transcriptional activity at the G(1)/S transition

Androgens are essential for the differentiation, growth, and maintenance of male-specific organs. The effects of androgens in cells are mediated by the androgen receptor (AR), a member of the nuclear receptor superfamily of transcription factors. Recently, transient transfection studies have shown that overexpression of cell cycle regulatory proteins affects the transcriptional activity of the AR. In this report, we characterize the transcriptional activity of endogenous AR through the cell cycle. We demonstrate that in G0, AR enhances transcription from an integrated steroid-responsive mouse mammary tumor virus promoter and also from an integrated androgen-specific probasin promoter. This activity is strongly reduced or abolished at the G(1)/S boundary. In S phase, the receptor regains activity, indicating that there is a transient regulatory event that inactivates the AR at the G(1)/S transition. This regulation is specific for the AR, since the related glucocorticoid receptor is transcriptionally active at the G(1)/S boundary. Not all of the effects of androgens are blocked, however, since androgens retain the ability to increase AR protein levels. The transcriptional inactivity of the AR at the G(1)/S junction coincides with a decrease in AR protein level, although activity can be partly rescued without an increase in receptor. Inhibition of histone deacetylases brings about this partial restoration of AR activity at the G(1)/S boundary, demonstrating the involvement of acetylation pathways in the cell cycle regulation of AR transcriptional activity. Finally, a model is proposed that explains the inactivity of the AR at the G(1)/S transition by integrating receptor levels, the action of cell cycle regulators, and the contribution of histone acetyltransferase-containing coactivators.

NUREBASE: database of nuclear hormone receptors

Nuclear hormone receptors are an abundant class of ligand activated transcriptional regulators, found in varying numbers in all animals. Based on our experience of managing the official nomenclature of nuclear receptors, we have developed NUREBASE, a database containing protein and DNA sequences, reviewed protein alignments and phylogenies, taxonomy and annotations for all nuclear receptors. The reviewed NUREBASE is completed by NUREBASE_DAILY, automatically updated every 24 h. Both databases are organized under a client/server architecture, with a client written in Java which runs on any platform. This client, named FamFetch, integrates a graphical interface allowing selection of families, and manipulation of phylogenies and alignments. NUREBASE sequence data is also accessible through a World Wide Web server, allowing complex queries. All information on accessing and installing NUREBASE may be found at http://www.ens-lyon.fr/LBMC/laudet/nurebase.html.

Collecting and harvesting biological data: the GPCRDB and NucleaRDB information systems

The amount of genomic and proteomic data that is entered each day into databases and the experimental literature is outstripping the ability of experimental scientists to keep pace. While generic databases derived from automated curation efforts are useful, most biological scientists tend to focus on a class or family of molecules and their biological impact. Consequently, there is a need for molecular class-specific or other specialized databases. Such databases collect and organize data around a single topic or class of molecules. If curated well, such systems are extremely useful as they allow experimental scientists to obtain a large portion of the available data most relevant to their needs from a single source. We are involved in the development of two such databases with substantial pharmacological relevance. These are the GPCRDB and NucleaRDB information systems, which collect and disseminate data related to G protein-coupled receptors and intra-nuclear hormone receptors, respectively. The GPCRDB was a pilot project aimed at building a generic molecular class-specific database capable of dealing with highly heterogeneous data. A first version of the GPCRDB project has been completed and it is routinely used by thousands of scientists. The NucleaRDB was started recently as an application of the concept for the generalization of this technology. The GPCRDB is available via the WWW at http://www.gpcr.org/7tm/ and the NucleaRDB at http://www.receptors.org/NR/.

Functional analysis of R651 mutations in the putative helix 6 of rat glucocorticoid receptors

Trypsin digestion of steroid-free, but not steroid-bound, rat glucocorticoid receptor (GR) has recently been reported to occur at arginine-651 (R651). This residue is close to the affinity labeled Cys-656 and thus could be a sensitive probe of steroid binding. This hypothesis is supported by the current model of the GR ligand binding domain (LBD), which is based on the X-ray structures of several related receptor LBDs and places R651 in the middle of the putative alpha-helix 6 (649-EQRMS-653 of rat GR), close to the bound steroid. To test this model, R651, which could be involved in hydrophilic and/or hydrogen bonding, was mutated to alanine (A), which favors alpha-helices, the helix breakers proline (P) and glycine (G), or tryptophan (W). All receptors were expressed at about the same level, as determined by Western blots, but the cell-free binding activity of R651P was reduced twofold. The cell-free binding affinities were all within a factor of 10 of wild type receptors. Whole cell biological activity with transiently transfected receptors was determined with a variety of GR agonists (dexamethasone and deacylcortivazol) or antagonists (dexamethasone mesylate, RU486, and progesterone). Reporters containing both simple (GRE) and complex (MMTV) enhancers were used to test for alterations in GR interactions with enhancer/promoter complexes. Surprisingly, no correlation was observed between biological activity and ability to preserve alpha-helical structures for each point mutation. Finally, similar trypsin digestion patterns indicated no major differences in the tertiary structure of the mutant receptors. Collectively, these results argue that the polar/ionizable residue R651 is not required for GR activity and is not part of an alpha-helix in the steroid-free or bound GR. The effect of these mutations on GR structure and activity may result from a cascade of initially smaller perturbations. These LBD alterations were the most varied for interactions with deacylcortivazol and RU 486, which have recently been predicted to be sub-optimal binders due to their large size. However, further analyses of ligand size versus affinity suggest that there is no narrowly defined optimal size for ligand binding, although larger ligands may be more sensitive to modifications of LBD structure. Finally, the changes in GR activity with the various mutations seem to result from altered LBD interactions with common, as opposed to enhancer specific, transcription factors.

OOTFD (Object-Oriented Transcription Factors Database): an object-oriented successor to TFD

ooTFD (object-oriented Transcription Factors Database) is a successor to TFD (Transcription Factors Database). ooTFD contains information represented in TFD but also allows the representation of containment, composite, and interaction relationships between transcription factor polypeptides. ooTFD is designed to represent information about all transcription factors, both eukaryotic and prokaryotic, basal as well as regulatory factors, and multiprotein complexes as well as monomers. ooTFD and associated tools and services can be accessed at http://www.isbi.net/