Completion of Hepatitis C Virus Replication Cycle in Heterokaryons Excludes Dominant Restrictions in Human Non-liver and Mouse Liver Cell Lines

Hepatitis C virus (HCV) is hepatotropic and only infects humans and chimpanzees. Consequently, an immunocompetent small animal model is lacking. The restricted tropism of HCV likely reflects specific host factor requirements. We investigated if dominant restriction factors expressed in non-liver or non-human cell lines inhibit HCV propagation thus rendering these cells non-permissive. To this end we explored if HCV completes its replication cycle in heterokaryons between human liver cell lines and non-permissive cell lines from human non-liver or mouse liver origin. Despite functional viral pattern recognition pathways and responsiveness to interferon, virus production was observed in all fused cells and was only ablated when cells were treated with exogenous interferon. These results exclude that constitutive or virus-induced expression of dominant restriction factors prevents propagation of HCV in these cell types, which has important implications for HCV tissue and species tropism. In turn, these data strongly advocate transgenic approaches of crucial human HCV cofactors to establish an immunocompetent small animal model

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