PNNL

Abstract

Direct-acting antivirals for the treatment of COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, 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 ligands with noncovalent interactions to SARS-CoV-2 Mpro, here we developed a closed-loop 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 inhibitors and incorporates cutting-edge experimental techniques for validation. Through this process, promising candidates in the library were screened and several potential hits were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities using our pipeline, the best inhibitor having a KI of 5.27 uM. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography. The induced conformational changes based on molecular dynamics simulations suggest that the dynamics may be an important factor to consider 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 design and provide a basis for further lead optimization in a closed loop.