Small-molecule modulators of TRMT2A decrease PolyQ aggregation and PolyQ-induced cell death.

Polyglutamine (polyQ) diseases are characterized by an expansion of cytosine-adenine-guanine (CAG) trinucleotide repeats encoding for an uninterrupted prolonged polyQ tract. We previously identified TRMT2A as a strong modifier of polyQ-induced toxicity in an unbiased large-scale screen in Drosophila melanogaster. This work aimed at identifying and validating pharmacological TRMT2A inhibitors as treatment opportunities for polyQ diseases in humans. Computer-aided drug discovery was implemented to identify human TRMT2A inhibitors. Additionally, the crystal structure of one protein domain, the RNA recognition motif (RRM), was determined, and Biacore experiments with the RRM were performed. The identified molecules were validated for their potency to reduce polyQ aggregation and polyQ-induced cell death in human HEK293T cells and patient derived fibroblasts. Our work provides a first step towards pharmacological inhibition of this enzyme and indicates TRMT2A as a viable drug target for polyQ diseases.

The Conorfamide RPRFa Stabilizes the Open Conformation of Acid-Sensing Ion Channel 3 via the Nonproton Ligand-Sensing Domain.

Acid-sensing ion channel 3 (ASIC3) is a proton-gated Na+ channel with important roles in pain. ASIC3 quickly desensitizes in less than a second, limiting its capacity to sense sustained acidosis during pain. RFamide neuropeptides are modulators of ASIC3 that slow its desensitization and induce a variable sustained current. The molecular mechanism of slowed desensitization and the RFamide binding site on ASIC3 are unknown. RPRFamide, a RFamide from the venom of a cone snail, has a comparatively high affinity for ASIC3 and strongly slows its desensitization. Here we show that covalent binding of a UV-sensitive RPRFamide variant to ASIC3 prevents desensitization, suggesting that RPRFamide has to unbind from ASIC3 before it can desensitize. Moreover, we show by in silico docking to a homology model of ASIC3 that a cavity in the lower palm domain, which is also known as the nonproton ligand-sensing domain, is a potential binding site of RPRFamide. Finally, using extensive mutagenesis of residues lining the nonproton ligand-sensing domain, we confirm that this domain is essential for RPRFamide modulation of ASIC3. As comparative analysis of ASIC crystal structures in the open and in the desensitized conformation suggests that the lower palm domain contracts during desensitization, our results collectively suggest that RPRFamide, and probably also other RFamide neuropeptides, bind to the nonproton ligand-sensing domain to stabilize the open conformation of ASIC3.

Heteroaromatic π-stacking energy landscapes.

In this study we investigate π-stacking interactions of a variety of aromatic heterocycles with benzene using dispersion corrected density functional theory. We calculate extensive potential energy surfaces for parallel-displaced interaction geometries. We find that dispersion contributes significantly to the interaction energy and is complemented by a varying degree of electrostatic interactions. We identify geometric preferences and minimum interaction energies for a set of 13 5- and 6-membered aromatic heterocycles frequently encountered in small drug-like molecules. We demonstrate that the electrostatic properties of these systems are a key determinant for their orientational preferences. The results of this study can be applied in lead optimization for the improvement of stacking interactions, as it provides detailed energy landscapes for a wide range of coplanar heteroaromatic geometries. These energy landscapes can serve as a guide for ring replacement in structure-based drug design.

Cleavage entropy as quantitative measure of protease specificity.

A purely information theory-guided approach to quantitatively characterize protease specificity is established. We calculate an entropy value for each protease subpocket based on sequences of cleaved substrates extracted from the MEROPS database. We compare our results with known subpocket specificity profiles for individual proteases and protease groups (e.g. serine proteases, metallo proteases) and reflect them quantitatively. Summation of subpocket-wise cleavage entropy contributions yields a measure for overall protease substrate specificity. This total cleavage entropy allows ranking of different proteases with respect to their specificity, separating unspecific digestive enzymes showing high total cleavage entropy from specific proteases involved in signaling cascades. The development of a quantitative cleavage entropy score allows an unbiased comparison of subpocket-wise and overall protease specificity. Thus, it enables assessment of relative importance of physicochemical and structural descriptors in protease recognition. We present an exemplary application of cleavage entropy in tracing substrate specificity in protease evolution. This highlights the wide range of substrate promiscuity within homologue proteases and hence the heavy impact of a limited number of mutations on individual substrate specificity.