データサイエンティスト、創薬のスピードアップに照準を定める(Data Scientist Fixes His Sights on Speeding Up Drug Discovery)

2024-11-25 パシフィック・ノースウェスト国立研究所(PNNL)

<関連情報>

• https://www.pnnl.gov/news-media/data-scientist-fixes-his-sights-speeding-drug-discovery
• https://link.springer.com/article/10.1007/s10822-023-00509-1
• https://pubs.acs.org/doi/full/10.1021/acs.jcim.2c01377

SARS-CoV-2メインプロテアーゼを標的とするリード化合物の最適化を加速するためのデータ駆動型アプローチと実験的アプローチの統合 Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease

Rohith Anand Varikoti, Katherine J. Schultz, Chathuri J. Kombala, Agustin Kruel, Kristoffer R. Brandvold, Mowei Zhou & Neeraj Kumar
Journal of Computer-Aided Molecular Design  Published:14 June 2023
DOI:https://doi.org/10.1007/s10822-023-00509-1

Abstract

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M^pro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC₅₀ values in the low micromolar range: 2.95±0.0017 μM and 3.41±0.0015 μM, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the M^pro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

AIが加速するSARS-CoV-2標的共有結合阻害剤の設計 AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Rajendra P. Joshi,Katherine J. Schultz,Jesse William Wilson,Agustin Kruel,Rohith Anand Varikoti,Chathuri J. Kombala,Daniel W. Kneller,Stephanie Galanie,Gwyndalyn Phillips,Qiu Zhang,Leighton Coates,Jyothi Parvathareddy,Surekha Surendranathan,Ying Kong,Austin Clyde,Arvind Ramanathan,Colleen B. Jonsson,Kristoffer R. Brandvold,Mowei Zhou,Martha S. Head,Andrey Kovalevsky,Neeraj Kumar
Journal of Chemical Information and Modeling  Published: February 21, 2023
DOI:https://doi.org/10.1021/acs.jcim.2c01377

Abstract

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 (M^pro), 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 M^pro with micromolar affinities (K_I 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 K_I 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.