Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Rohith Anand Varikoti, Katherine J. Schultz, Chathuri J. Kombala, Agustin Kruel, Kristoffer R. Brandvold, Mowei Zhou, Neeraj Kumar
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引用次数: 0

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 Mpro 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 IC\(_{50}\) values in the low micromolar range: \(2.95\pm 0.0017\) \(\upmu\)M and 3.41±0.0015 \(\upmu\)M, 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 Mpro. 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.

Abstract Image

整合数据驱动和实验方法,加速针对SARS-CoV-2主要蛋白酶的导联优化
通过将计算建模与领域感知机器学习(ML)模型相结合,然后以迭代的方式进行实验验证,可以加快潜在治疗候选者的识别。生成式深度学习模型可以生成数千个新的候选对象,然而,它们的物理化学和生物化学特性通常没有得到充分优化。使用我们最近开发的深度学习模型和支架作为起点,我们为SARS-CoV-2 Mpro生成了数万种化合物,这些化合物保留了核心支架。我们利用并实现了几种计算工具,如结构警报和毒性分析,高通量虚拟筛选,基于ml的3D定量结构-活性关系,多参数优化和图神经网络对生成的候选物进行提前预测生物活性和结合亲和力。作为这些综合计算努力的结果,8个有希望的候选者被挑选出来,并使用Native质谱法和基于fret的功能分析进行实验测试。两种具有喹唑啉-2-硫醇和乙酰胡椒啶核心部分的化合物显示IC\(_{50}\) 低微摩尔范围内的值: \(2.95\pm 0.0017\) \(\upmu\)M和3.41±0.0015 \(\upmu\)分别为M。分子动力学模拟进一步强调,这些化合物的结合导致B链和Mpro界面域内的变构调节。我们的集成方法为数据驱动先导优化提供了一个平台,可以在闭环中快速表征和实验验证,可应用于其他潜在的蛋白质靶点。
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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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