Human coronaviruses (CoVs) are distributed worldwide and cause a significant percentage of all common colds. Most of these viruses presumably emerged through zoonotic transmission, have adapted to the human host, and are now known to cause mild upper respiratory tract infections. However, the more recent zoonotic transmission of the highly pathogenic Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) have demonstrated the potential of coronaviruses to also cause highly severe human diseases and are associated with a high mortality. The precise mechanisms that allow coronaviruses to jump across species barriers are only poorly understood. "COV RESTRIC" aims to unravel species barriers of coronavirus infections and to uncover basic host cell mechanisms preventing a transmission across different species. We hypothesize that conserved restriction factors exist between different species which limit viral replication and which need to be overcome via viral evasion and adaptation strategies in order to establish a zoonotic infection. These may include the lack or genetic incompatibility of essential viral replication co-factors as well as the presence of antiviral restriction mechanisms. We will use two complementary genetic screening approaches to identify unknown coronavirus restriction factors. Once we have identified possible candidates, we will analyze them for their cross-conservation employing coronaviruses from different species, such as human, bat and camel virus isolates, as well as the respective primary cell culture material originating from the authentic host. This approach will help to rigorously define species barriers to viral transmission which should promote future development of preventive and therapeutic strategies to combat emerging RNA virus infections in humans.

Coronavirus 2019-nCoV has become a worldwide public health emergency, and the lack of vaccines and drugs to immediately address this outbreak is painfully clear. Even if the epidemic can be stopped, the virus may return in the same or a modified form. More than vaccines and therapeutic antibodies, antiviral drugs can target highly conserved viral functions and have the broad-spectrum activity that is critical to combat current and future outbreaks. Since the 2003 SARS outbreak, as leading academic coronavirus researchers, we have collaborated to understand and inhibit coronavirus replication. We defined viral key functions, developed tools for inhibitor screening, and identified/engineered drug candidates. Until 2015, our collaborative efforts were supported by the FP7 SILVER project, but they have been continued until this very day. As European coronavirus experts, we now propose the SCORE project, supported by a leading pharmaceutical company. Virologists, biochemists, structural biologists, and medicinal chemists will collaborate in a state-of-the-art drug discovery/design program that targets 2019-nCoV. Our vast SARS-CoV-derived expertise and unique toolbox will be a major asset to achieve immediate impact. We will target the virus using 5 independent approaches: (i) using of (combinations of) FDA-approved drugs, (ii) targeting viral RNA synthesis, (iii) inhibiting coronavirus proteases, (iv) blocking virus entry, (v) discovery and development of new antivirals. This program will be supplemented with 2019-nCoV toolbox and animal model development. We aim to deliver proof-of-concept for selected compounds within 6-9 months, after which they will be offered for further use/development. This will contribute to short-term solutions for the on-going crisis and also pave the way for mid/long-term success in developing inhibitors that will be active against (evolving) 2019-nCoV strains, other SARS-like coronaviruses, and potentially (beta)coronaviruses at large.

ESCAPE-2 will develop world-class, extreme-scale computing capabilities for European operational numerical weather and climate prediction, and provide the key components for weather and climate domain benchmarks to be deployed on extreme-scale demonstrators and beyond. This will be achieved by developing bespoke and novel mathematical and algorithmic concepts, combining them with proven methods, and thereby reassessing the mathematical foundations forming the basis of Earth system models. ESCAPE-2 also invests in significantly more productive programming models for the weather-climate community through which novel algorithm development will be accelerated and future-proofed. Eventually, the project aims at providing exascale-ready production benchmarks to be operated on extreme-scale demonstrators (EsD) and beyond. ESCAPE-2 combines cross-disciplinary uncertainty quantification tools (URANIE) for high-performance computing, originating from the energy sector, with ensemble based weather and climate models to quantify the effect of model and data related uncertainties on forecasting – a capability, which weather and climate prediction has pioneered since the 1960s. The mathematics and algorithmic research in ESCAPE-2 will focus on implementing data structures and tools supporting parallel computation of dynamics and physics on multiple scales and multiple levels. Highly-scalable spatial discretization will be combined with proven large time-stepping techniques to optimize both time-to-solution and energy-to-solution. Connecting multi-grid tools, iterative solvers, and overlapping computations with flexible-order spatial discretization will strengthen algorithm resilience against soft or hard failure. In addition, machine learning techniques will be applied for accelerating complex sub-components. The sum of these efforts will aim at achieving at the same time: performance, resilience, accuracy and portability.