KIM-1/TIM-1 is a Receptor for SARS-CoV-2 in Lung and Kidney.

SARS-CoV-2 precipitates respiratory distress by infection of airway epithelial cells and is often accompanied by acute kidney injury. We report that Kidney Injury Molecule-1/T cell immunoglobulin mucin domain 1 (KIM-1/TIM-1) is expressed in lung and kidney epithelial cells in COVID-19 patients and is a receptor for SARS-CoV-2. Human and mouse lung and kidney epithelial cells express KIM-1 and endocytose nanoparticles displaying the SARS-CoV-2 spike protein (virosomes). Uptake was inhibited by anti-KIM-1 antibodies and TW-37, a newly discovered inhibitor of KIM-1-mediated endocytosis. Enhanced KIM-1 expression by human kidney tubuloids increased uptake of virosomes. KIM-1 binds to the SARS-CoV-2 Spike protein in vitro . KIM-1 expressing cells, not expressing angiotensin-converting enzyme 2 (ACE2), are permissive to SARS-CoV-2 infection. Thus, KIM-1 is an alternative receptor to ACE2 for SARS-CoV-2. KIM-1 targeted therapeutics may prevent and/or treat COVID-19.

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening.

The unparalleled global effort to combat the continuing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic over the last year has resulted in promising prophylactic measures. However, a need still exists for cheap, effective therapeutics, and targeting multiple points in the viral life cycle could help tackle the current, as well as future, coronaviruses. Here, we leverage our recently developed, ultra-large-scale in silico screening platform, VirtualFlow, to search for inhibitors that target SARS-CoV-2. In this unprecedented structure-based virtual campaign, we screened roughly 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets. In addition to targeting the active sites of viral enzymes, we also targeted critical auxiliary sites such as functionally important protein-protein interactions.

A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 in silico hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.

Large Nanodiscs: A Potential Game Changer in Structural Biology of Membrane Protein Complexes and Virus Entry.

Phospho-lipid bilayer nanodiscs have gathered much scientific interest as a stable and tunable membrane mimetic for the study of membrane proteins. Until recently the size of the nanodiscs that could be produced was limited to ~ 16 nm. Recent advances in nanodisc engineering such as covalently circularized nanodiscs (cND) and DNA corralled nanodiscs (DCND) have opened up the possibility of engineering nanodiscs of size up to 90 nm. This enables widening the application of nanodiscs from single membrane proteins to investigating large protein complexes and biological processes such as virus-membrane fusion and synaptic vesicle fusion. Another aspect of exploiting the large available surface area of these novel nanodiscs could be to engineer more realistic membrane mimetic systems with features such as membrane asymmetry and curvature. In this review, we discuss the recent technical developments in nanodisc technology leading to construction of large nanodiscs and examine some of the implicit applications.

An open-source drug discovery platform enables ultra-large virtual screens.

On average, an approved drug currently costs US$2-3 billion and takes more than 10 years to develop1. In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant (Kd) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins.

Hypoxia-inducible lipid droplet-associated protein inhibits adipose triglyceride lipase.

Elaborate control mechanisms of intracellular triacylglycerol (TAG) breakdown are critically involved in the maintenance of energy homeostasis. Hypoxia-inducible lipid droplet-associated protein (HILPDA)/hypoxia-inducible gene-2 (Hig-2) has been shown to affect intracellular TAG levels, yet, the underlying molecular mechanisms are unclear. Here, we show that HILPDA inhibits adipose triglyceride lipase (ATGL), the enzyme catalyzing the first step of intracellular TAG hydrolysis. HILPDA shares structural similarity with G0/G1 switch gene 2 (G0S2), an established inhibitor of ATGL. HILPDA inhibits ATGL activity in a dose-dependent manner with an IC50 value of ∼2 µM. ATGL inhibition depends on the direct physical interaction of both proteins and involves the N-terminal hydrophobic region of HILPDA and the N-terminal patatin domain-containing segment of ATGL. Finally, confocal microscopy combined with Förster resonance energy transfer-fluorescence lifetime imaging microscopy analysis indicated that HILPDA and ATGL colocalize and physically interact intracellularly. These findings provide a rational biochemical explanation for the tissue-specific increased TAG accumulation in HILPDA-overexpressing transgenic mouse models.