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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.
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http://dx.doi.org/10.1126/sciadv.abf8711 | DOI Listing |
J Comput Chem
September 2025
Johnson & Johnson, Beerse, Belgium.
Herein we report the in silico discovery of 13 novel micromolar potent inhibitors of the SARS-CoV-2 NSP13 helicase validated in cellular antiviral and biophysical ThermoFluor assays. The compounds, discovered using a novel fragment-based pharmacophore virtual screening workflow named FragmentScout, enable the advancement of novel antiviral agents. FragmentScout uses publicly accessible structural data of the SARS-CoV-2 NSP13 helicase, which was previously generated at the Diamond LightSource by XChem high-throughput crystallographic fragment screening.
View Article and Find Full Text PDFJ Med Chem
September 2025
Center for Advanced Biotechnology and Medicine, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, United States.
Dengue viruses (DENVs) infect approximately 400 million people each year, and currently, there are no effective therapeutics available. To explore potential starting points for antiviral drug development, we conducted a large-scale crystallographic fragment screen targeting the RNA-dependent RNA polymerase (RdRp) domain of the nonstructural protein 5 (NS5) from DENV serotype 2. Our screening, which involved 1108 fragments, identified 60 hit compounds across various known binding sites, including the active site, N pocket, and RNA tunnel.
View Article and Find Full Text PDFAdv Sci (Weinh)
August 2025
School of Advanced Energy, Sun Yat-Sen University, Shenzhen, Guangdong, 518107, China.
Overcoming the capacity-stability-cost trilemma in hydrogen storage materials represents a fundamental Pareto-type challenge for practical metal hydride applications. Current research efforts remain fragmented, typically pursuing single-parameter optimization while lacking holistic approaches that concurrently satisfy all three criteria. Here, a novel design paradigm is proposed by orchestrating A/B-side multi-principal-element alloys (MPEAs) in C14 Laves phases, enabling concurrent optimization of interstitial hydrogen storage environments and thermodynamics.
View Article and Find Full Text PDFJACS Au
August 2025
Xi'an Modern Chemistry Research Institute, Xi'an 710065, China.
High-performance insensitive energetic materials have long been a central focus of energetic materials research. To effectively balance high energy density and insensitivity, a structure-based screening was performed using the Cambridge Crystallographic Data Centre database. Consequently, a strategy enhancing the stability of energetic compounds through supramolecular assembly based on self-complementary hydrogen bonding was developed.
View Article and Find Full Text PDFNat Commun
August 2025
Department of Physics, University of Cambridge, Cambridge, UK.
The efficient separation of chiral molecules is a fundamental challenge in the manufacture of pharmaceuticals and light-polarising materials. We developed an approach that combines machine learning with a physics-based representation to predict resolving agents for chiral molecules, using a transformer-based neural network. In retrospective tests, our approach reaches a four to six-fold improvement over the historical - trial and error based - hit rate.
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