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.

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.

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.

Prolonged acidosis, as it occurs during ischemic stroke, induces neuronal death via acid-sensing ion channel 1a (ASIC1a). Concomitantly, it desensitizes ASIC1a, highlighting the pathophysiological significance of modulators of ASIC1a acid sensitivity. One such modulator is the opioid neuropeptide big dynorphin (Big Dyn) which binds to ASIC1a and enhances its activity during prolonged acidosis. The molecular determinants and dynamics of this interaction remain unclear, however. Here, we present a molecular interaction model showing a dynorphin peptide inserting deep into the acidic pocket of ASIC1a. We confirmed experimentally that the interaction is predominantly driven by electrostatic forces, and using noncanonical amino acids as photo-cross-linkers, we identified 16 residues in ASIC1a contributing to Big Dyn binding. Covalently tethering Big Dyn to its ASIC1a binding site dramatically decreased the proton sensitivity of channel activation, suggesting that Big Dyn stabilizes a resting conformation of ASIC1a and dissociates from its binding site during channel opening.

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Novel therapeutic approaches are being developed to tackle neurodegenerative diseases, due to the lack of efficiency of the known druggable targets. For Huntington's disease, a promising approach is the regulation of the RNA product. This target would allow for a selective and effective inhibition of the toxic effects exerted by the final nucleic product and the coded protein. In this review, the current state of the art of RNA regulation is discussed, with a brief but insightful view on novel plausible targets. After this, an emphasis on successful computational and experimental approaches tailored in modeling and regulating RNA aberrant behavior are extensively presented. Finally, the application and limitations of current computational methods are discussed, and possible avenues for improvement are outlined.

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