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.

The use of in vitro peptide binding profiles and in silico ligand-receptor interaction profiles to describe ligand-induced conformations of the retinoid X receptor alpha ligand-binding domain.

It is hypothesized that different ligand-induced conformational changes can explain the different interactions of nuclear receptors with regulatory proteins, resulting in specific biological activities. Understanding the mechanism of how ligands regulate cofactor interaction facilitates drug design. To investigate these ligand-induced conformational changes at the surface of proteins, we performed a time-resolved fluorescence resonance energy transfer assay with 52 different cofactor peptides measuring the ligand-induced cofactor recruitment to the retinoid X receptor-alpha (RXRalpha) in the presence of 11 compounds. Simultaneously we analyzed the binding modes of these compounds by molecular docking. An automated method converted the complex three-dimensional data of ligand-protein interactions into two-dimensional fingerprints, the so-called ligand-receptor interaction profiles. For a subset of compounds the conformational changes at the surface, as measured by peptide recruitment, correlate well with the calculated binding modes, suggesting that clustering of ligand-receptor interaction profiles is a very useful tool to discriminate compounds that may induce different conformations and possibly different effects in a cellular environment. In addition, we successfully combined ligand-receptor interaction profiles and peptide recruitment data to reveal structural elements that are possibly involved in the ligand-induced conformations. Interestingly, we could predict a possible binding mode of LG100754, a homodimer antagonist that showed no effect on peptide recruitment. Finally, the extensive analysis of the peptide recruitment profiles provided novel insight in the potential cellular effect of the compound; for the first time, we showed that in addition to the induction of coactivator peptide binding, all well-known RXRalpha agonists also induce binding of corepressor peptides to RXRalpha.

The nuclear receptor ligand-binding domain: a family-based structure analysis.

Nuclear receptors (NRs) are ligand-dependent transcription factors that play a central role in various physiological processes. The pharmaceutical industry has great interest in this gene-family for the discovery of novel or improved drugs for treatment of, for example, cancer, infertility, or diabetes. The usage of three-dimensional coordinates of protein structures to analyse and predict interactions with ligands is an important aspect of this process. All NR ligand-binding domains have a similar fold, which allows for comparison of the structures of their three main functional sites: the ligand-binding pocket, the cofactor-binding groove, and the dimerization interface. We performed an analysis of nearly one hundred NR ligand-binding domain structures, and identified the functionally important residues. The combined knowledge about the shape of the binding sites and the residues involved in the binding is important for drug design in two ways. First, knowledge about the location of residues that interact with a ligand in all crystal structures or in certain subfamilies assists in the design and docking of drugs. Second, similarities and differences in the residue types of the most frequent ligand- and cofactor-binding residues provide insight about potential cross-reactivity of ligands or cofactors.

A family-based approach reveals the function of residues in the nuclear receptor ligand-binding domain.

Literature studies, 3D structure data, and a series of sequence analysis techniques were combined to reveal important residues in the structure and function of the ligand-binding domain of nuclear hormone receptors. A structure-based multiple sequence alignment allowed for the seamless combination of data from many different studies on different receptors into one single functional model. It was recently shown that a combined analysis of sequence entropy and variability can divide residues in five classes; (1) the main function or active site, (2) support for the main function, (3) signal transduction, (4) modulator or ligand binding and (5) the rest. Mutation data extracted from the literature and intermolecular contacts observed in nuclear receptor structures were analyzed in view of this classification and showed that the main function or active site residues of the nuclear receptor ligand-binding domain are involved in cofactor recruitment. Furthermore, the sequence entropy-variability analysis identified the presence of signal transduction residues that are located between the ligand, cofactor and dimer sites, suggesting communication between these regulatory binding sites. Experimental and computational results agreed well for most residues for which mutation data and intermolecular contact data were available. This allows us to predict the role of the residues for which no functional data is available yet. This study illustrates the power of family-based approaches towards the analysis of protein function, and it points out the problems and possibilities presented by the massive amounts of data that are becoming available in the "omics era". The results shed light on the nuclear receptor family that is involved in processes ranging from cancer to infertility, and that is one of the more important targets in the pharmaceutical industry.

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/.