严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
2019年冠状病毒病(COVID-19)
共价键
2019-20冠状病毒爆发
化学
计算生物学
病毒学
医学
生物
传染病(医学专业)
内科学
爆发
疾病
有机化学
作者
Ritesh Joshi,Katherine J. Schultz,Jesse Wilson,Agustin Kruel,Rohith Anand Varikoti,Chathuri Kombala,D.W. Kneller,Stephanie Galanie,G.N. Phillips,Qiu Zhang,Leighton Coates,Jyothi Parvathareddy,Surekha Surendranathan,Ying Kong,Austin Clyde,Arvind Ramanathan,Colleen B. Jonsson,Kristoffer Brandvold,Mowei Zhou,Martha S. Head,Andrey Kovalevsky,Neeraj Kumar
标识
DOI:10.1021/acs.jcim.2c01377
摘要
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. 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.
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