Identifying Potential Antiviral Small Molecules Against the Coronavirus Disease 2019
Felice Lightstone | 20-ERD-065
As coronavirus spread in 2020 and continued into 2021, the world needed a rapid response to create a medical countermeasure against this long-lasting pandemic. Toward that end, our goal was to develop a platform for small molecule screening to identify potential candidate compounds that can be developed into antiviral drugs. Specifically, we used Livermore's existing computational drug discovery tools, adapted them for the scale and use case, and screened 1.6 billion small molecules virtually from public databases to predict which ones would have the best ability to bind to two proteins of SARS-CoV-2, the virus that causes COVID-19. In doing so, we optimized our methods for screening and downselecting compounds for experimental testing. Specifically, we used molecular docking, single-point molecular mechanics/generalized Born surface area continuum calculations, structure-based deep fusion inference models, and molecular dynamics simulations to predict efficacious binding. After purchasing 1,681 of the best predicted binding candidates, we subsequently used biolayer interferometry competition assays for spike protein and fluorescence resonance energy transfer- (FRET-) based activity assay for main protease (Mpro) to test the compounds' effectiveness at binding to the respective proteins. Positive results were tested in surrogate pseudovirus infection assays or live virus. Of 1,172 compounds tested against Mpro at 100 µM (micromolar), 78 (6.6%) compounds inhibited its activity at least 30% and 29 (2.5%) by at least 50%. Of 509 compounds predicted to bind the spike protein and tested at 10 µM in a VSV- (vesicular stomatitis virus-) spike pseudovirus infection assay, 74 (14.5%) compounds inhibited infection at least 30% and 23 (4.5%) by at least 50%. These yields are a great improvement over those from a standard brute force screening of ~12,000 compounds that yielded 0.8% (published in Nature). Compound libraries, protein targets, and co-complex results from our study are available online at https://covid19drugscreen.llnl.gov. These compounds continue to be developed under externally funded projects. Most importantly, we were able to create a viable small molecule screening platform for drug discovery with validated methods.
Creating a high-throughput screening platform based on physics methods and machine learning algorithms for future medical countermeasure design strengthens the nation's pandemic preparedness or readiness for any emerging biological threat, and advances Lawrence Livermore National Laboratory's mission in biosecurity. The need for this platform arose from the COVID-19 pandemic, and creating it for this LDRD project confirmed the relevance of Livermore's methods and technological expertise. We were able to validate many of our computational methods with experimental results and identify areas of improvement. The project established the Laboratory's ability to work in multidisciplinary teams and continue to build its reputation in technologies for medical countermeasure design. This effort is aligned with new mission areas identified by the Laboratory director. Pandemic preparedness is also an emphasis of the current administration. This research positions the Laboratory to respond to relevant future funding calls.
Publications, Presentations, and Patents
G. A. Stevenson, D. Jones, H. Kim, W. F. D. Bennett, B. J. Bennion, M. Borucki, F. Bourguet, A. Epstein, M. Franco, B. Harmon, S. He, M. P. Katz, D. Kirshner, V. Lao, E. Y. Lau, J. Lo, K. McLoughlin, R. Mosesso, D. K. Murugesh, O. A. Negrete, E. A. Saada, B. Segelke, M. Stefan, M. W. Torres, D. Weilhammer, S. Wong, Y. Yang, A. Zemla, X. Zhang, F. Zhu, F. C. Lightstone, J. E. Allen (2021). "High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models," SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Article No. 74, 1-13.
E. Y. Lau, O. A. Negrete, W. F. D.Bennett, B. J. Bennion, M. Borucki, F. Bourguet, A. Epstein, M. Franco, B. Harmon, S. He, D. Jones, H. Kim, D. Kirshner, V. Lao, J. Lo, K. McLoughlin, R. Mosesso, D. K. Murugesh, E. A. Saada, B. Segelke, M. A. Stefan, G. A. Stevenson, M. W. Torres, D. R. Weilhammer, S. Wong, Y. Yang, A. Zemla, X. Zhang, F. Zhu, J. E. Allen, F. C. Lightstone (2021). "Discovery of Small-molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline," Frontiers in Molecular Biosciences, 8, 644.
Webpage: Lawrence Livermore National Laboratory Covid-19 Therapeutic Design (2020) DOI: 10.11578/1608139 LLNL-ABS-829457.