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AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Journal Article · · Journal of Chemical Information and Modeling
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  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Washington State Univ., Spokane, WA (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); US Department of Energy (USDOE), Washington DC (United States)
  4. US Department of Energy (USDOE), Washington DC (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Merck & Co., Inc., Rahway, NJ (United States)
  5. US Department of Energy (USDOE), Washington DC (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  6. University of Tennessee Health Science Center, Memphis, TN (United States)
  7. US Department of Energy (USDOE), Washington DC (United States); Argonne National Lab. (ANL), Lemont, IL (United States)
  8. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Washington State Univ., Spokane, WA (United States)
  9. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); US Department of Energy (USDOE), Washington DC (United States)
  10. US Department of Energy (USDOE), Washington DC (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Joint Institute for Biological Sciences; Amgen Research, Thousand Oaks, CA (United States)
+ Show Author Affiliations

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Argonne National Laboratory (ANL), Argonne, IL (United States); Environmental Molecular Sciences Laboratory (EMSL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
AC05-00OR22725; AC05-76RL01830; AC02-06CH11357; AC05-76RLO1830
OSTI ID:
1961968
Alternate ID(s):
OSTI ID: 1963814; OSTI ID: 2320226
Report Number(s):
PNNL-SA-176233
Journal Information:
Journal of Chemical Information and Modeling, Vol. 63, Issue 5; ISSN 1549-9596
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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60 APPLIED LIFE SCIENCES
inhibitors
ligands
oligomers
peptides and proteins
screening assays
main protease
SARS coronavirus
machine learning
artificial intelligence
automated covalent docking workflow
molecular dynamics
native mass spectrometry
room-temperature X-ray crystallography