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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.

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.

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.

Miniproteins featuring a stable α-helical motif allow exploring point mutations in and around FXXLF motifs to improve androgen receptor affinity.

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