Illustration of a computational pipeline for evaluating cyclodextrin host-guest complex formation through conformational capture of bullvalene

Cyclodextrins have a diverse range of applications, including as supramolecular hosts, as enzyme active-site analogs, in improving drug solubility and delivery, and in molecular selection. We have investigated their ability to form stable complexes with bullvalenes, unusual organic cage molecules that spontaneously interconvert between numerous degenerate isomers. The shape-shifting nature of substituted bullvalenes raises the potential for dynamic adaptive binding to biological targets. We tested whether beta- and gamma -cyclodextrins can capture particular bullvalene isomers and whether the preferred binding mode(s) differ between isomers. We first applied our computational host-guest interaction potential energy profiling to determine the best binding mode(s) of unsubstituted bullvalene and each isomer of methylenehydroxybullvalene to beta- and gamma -cyclodextrin. Subsequent molecular dynamics simulations of the predicted host-guest complexes showed that while unsubstituted bullvalene has a single, albeit ill-defined, binding mode with either cyclodextrin, each isomer of methylenehydroxybullvalene has two possible modes of binding to beta -cyclodextrin but only a single, nebulous mode of binding to gamma -cyclodextrin. Experimental determination of the binding free energy of each methylenehydroxybullvalene-cyclodextrin complex showed that methylenehydroxybullvalene is more likely to bind to beta -cyclodextrin than to gamma -cyclodextrin, despite its smaller cavity. Together, our results suggest that beta -cyclodextrin, but not gamma -cyclodextrin, shows promise for conformational capture of mono-substituted bullvalenes. More broadly, our computational pipeline should prove useful for rapid characterization of cyclodextrin host-guest complexes, avoiding the need for costly synthesis of guest molecules that are unlikely to bind stably, as well as providing detailed atomic-level insight into the nature of complexation.