Exploring the Fasciola hepatica tegument proteome.

The surface tegument of the liver fluke Fasciola hepatica is a syncytial cytoplasmic layer bounded externally by a plasma membrane and covered by a glycocalyx, which constitutes the interface between the parasite and its ruminant host. The tegument?s interaction with the immune system during the fluke?s protracted migration from the gut lumen through the peritoneal cavity and liver parenchyma to the lumen of the bile duct, plays a key role in the fluke?s establishment or elimination. However, little is known about proteins of the tegument surface or its secretions. We applied techniques developed for the blood fluke, Schistosoma mansoni, to enrich a tegument surface membrane preparation and analyse its composition by tandem mass spectrometry using new transcript databases for F. hepatica. We increased the membrane and secretory pathway components of the final preparation to _30%, whilst eliminating contaminating proteases. We identified a series of proteins or transcripts shared with the schistosome tegument including annexins, a tetraspanin, carbonic anhydrase and an orthologue of a host protein (CD59) that inhibits complement fixation. Unique to F. hepatica, we also found proteins with lectin, cubulin and von Willebrand factor domains plus 10 proteins with leader sequences or transmembrane helices. Many of these surface proteins are potential vaccine candidates. We were hampered in collecting tegument secretions by the propensity of liver flukes, unlike blood flukes, to vomit their gut contents. We analysed both the ?vomitus? and a second supernatant released from haematin-depleted flukes. We identified many proteases, some novel, as well as a second protein with a von Willebrand factor domain. This study demonstrates that components of the tegumental surface of F. hepatica can be defined using proteomic approaches, but also indicates the need to prevent vomiting if tegument secretions are to be characterised

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery