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SARS-CoV-2 infection causes spike-dependent fusion of infected cells with ACE2 positive neighboring cells, generating multi-nuclear syncytia that are often associated with severe COVID. To better elucidate the mechanism of spike-induced syncytium formation, we combine chemical genetics with 4D confocal imaging to establish the cell surface heparan sulfate (HS) as a critical stimulator for spike-induced cell-cell fusion. We show that HS binds spike and promotes spike-induced ACE2 clustering, forming synapse-like cell-cell contacts that facilitate fusion pore formation between ACE2-expresing and spike-transfected human cells. Chemical or genetic inhibition of HS mitigates ACE2 clustering, and thus, syncytium formation, whereas in a cell-free system comprising purified HS and lipid-anchored ACE2, HS stimulates ACE2 clustering directly in the presence of spike. Furthermore, HS-stimulated syncytium formation and receptor clustering require a conserved ACE2 linker distal from the spike-binding site. Importantly, the cell fusion-boosting function of HS can be targeted by an investigational HS-binding drug, which reduces syncytium formation in vitro and viral infection in mice. Thus, HS, as a host factor exploited by SARS-CoV-2 to facilitate receptor clustering and a stimulator of infection-associated syncytium formation, may be a promising therapeutic target for severe COVID.

 

Calculations with high accuracy for atomic and inter-atomic properties, such as nuclear magnetic resonance (NMR) spectroscopy and bond dissociation energies (BDEs) are valuable for pharmaceutical molecule structural analysis, drug exploration, and screening. It is important that these calculations should include relativistic effects, which are computationally expensive to treat. Non-relativistic calculations are less expensive but their results are less accurate. In this study, we present a computational framework for predicting atomic and inter-atomic properties by using machine-learning in a non-relativistic but accurate and computationally inexpensive framework. The accurate atomic and inter-atomic properties are obtained with a low dimensional deep neural network (DNN) embedded in a fragment-based graph convolutional neural network (F-GCN). The F-GCN acts as an atomic fingerprint generator that converts the atomistic local environments into data for the DNN, which improves the learning ability, resulting in accurate results as compared to experiments. Using this framework, the 13C/1H NMR chemical shifts of Nevirapine and phenol O–H BDEs are predicted to be in good agreement with experimental measurement.

 

Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.