Identification of acrylamide-based covalent inhibitors of SARS-CoV-2 (SCoV-2) Nsp15 using high-throughput screening and machine learning

Teena Bajaj; Babak Mosavati; Lydia H. Zhang; Mohammad S. Parsa; Huanchen Wang; Evan M. Kerek; Xueying Liang; Seyed Amir Tabatabaei Dakhili; Eddie Wehri; Silin Guo; Rushil N. Desai; Lauren M. Orr; Mohammad R. K. Mofrad; Julia Schaletzky; John R. Ussher; Xufang Deng; Robin Stanley; Basil P. Hubbard; Daniel K. Nomura; Niren Murthy

doi:10.1039/D4RA06955B

Identification of acrylamide-based covalent inhibitors of SARS-CoV-2 (SCoV-2) Nsp15 using high-throughput screening and machine learning†

Teena Bajaj,‡^a Babak Mosavati,‡^bc Lydia H. Zhang,^d Mohammad S. Parsa,

^e Huanchen Wang,^f Evan M. Kerek,^g Xueying Liang,^h Seyed Amir Tabatabaei Dakhili,ⁱ Eddie Wehri,^j Silin Guo,^k Rushil N. Desai,^c Lauren M. Orr,

^k Mohammad R. K. Mofrad,

^l Julia Schaletzky,^jm John R. Ussher,ⁱ Xufang Deng,^h Robin Stanley,^f Basil P. Hubbard,

^g Daniel K. Nomura

^k and Niren Murthy

*^h

Author affiliations

* Corresponding authors

^a Graduate Program of Comparative Biochemistry, University of California, Berkeley, Berkeley, CA, USA
E-mail: bajajtiya@berkeley.edu

^b Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
E-mail: bmosavati@berkeley.edu

^c Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA

^d Graduate Program of Molecular Toxicology, University of California, Berkeley, Berkeley, CA, USA

^e Department of Applied Science and Technology, University of California, Berkeley, CA, USA

^f Signal Transduction Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, North Carolina, USA

^g Department of Pharmacology, Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alberta, Canada

^h Department of Physiological Sciences, College of Veterinary Medicine, Oklahoma Center for Respiratory and Infectious Disease, Oklahoma State University, Oklahoma, USA
E-mail: nmurthy@berkeley.edu

ⁱ Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada

^j The Henry Wheeler Center for Emerging and Neglected Diseases, University of California, Berkeley, Berkeley, CA, USA

^k Department of Chemistry, University of California, Berkeley, Berkeley, CA, USA

^l Department of Mechanical Engineering, University of California, Berkeley, CA, USA

^m The Molecular Therapeutics Initiative, University of California, Berkeley, 344 Li Ka Shing, Berkeley, CA, USA

Abstract

Non-structural protein 15 (Nsp15) is a SARS-CoV-2 (SCoV-2) endoribonuclease and is a promising target for drug development because of its essential role in evading the host immune system. However, developing inhibitors against Nsp15 has been challenging due to its structural complexity and large RNA binding surface. In this report, we screened a 2640 acrylamide-based compound library against Nsp15 and identified 10 fragments that reacted with cysteine residues on Nsp15 and inhibited its endoribonuclease activity with IC₅₀s less than 5 μM. These compounds had several attractive properties, such as low molecular weight (180–300 g mol⁻¹), log P <3, zero violations to Lipinski's rules, and no apparent pan-assay interference (PAINs) properties. In addition, based on this data as a training set, we developed an artificial intelligence (AI) model that accelerated the hit to lead process and had a 73% accuracy for predicting new acrylamide-based Nsp15 inhibitors. Collectively, these results demonstrate that acrylamide fragments have great potential for developing Nsp15 inhibitors.

RSC Advances

Identification of acrylamide-based covalent inhibitors of SARS-CoV-2 (SCoV-2) Nsp15 using high-throughput screening and machine learning†

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Identification of acrylamide-based covalent inhibitors of SARS-CoV-2 (SCoV-2) Nsp15 using high-throughput screening and machine learning

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