Activation Route of the Schistosoma mansoni Cathepsin B1 Drug Target Structural Map with a Glycosaminoglycan Switch

SummaryCathepsin B1 (SmCB1) is a digestive protease of the parasitic blood fluke Schistosoma mansoni and a drug target for the treatment of schistosomiasis, a disease that afflicts over 200 million people. SmCB1 is synthesized as an inactive zymogen in which the N-terminal propeptide blocks the active site. We investigated the activation of the zymogen by which the propeptide is proteolytically removed and its regulation by sulfated polysaccharides (SPs). We determined crystal structures of three molecular forms of SmCB1 along the activation pathway: the zymogen, an activation intermediate with a partially cleaved propeptide, and the mature enzyme. We demonstrate that SPs are essential for the autocatalytic activation of SmCB1, as they interact with a specific heparin-binding domain in the propeptide. An alternative activation route is mediated by an S. mansoni asparaginyl endopeptidase (legumain) which is downregulated by SPs, indicating that SPs act as a molecular switch between both activation mechanisms

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures