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

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[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]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.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

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.

Profiling rhythmicity of bile salt hydrolase activity in the gut lumen with a rapid fluorescence assay.

Diurnal rhythmicity of cellular function is key to survival for most organisms on Earth. Many circadian functions are driven by the brain, but regulation of a separate set of peripheral rhythms remains poorly understood. The gut microbiome is a potential candidate for regulation of host peripheral rhythms, and this study sought to specifically examine the process of microbial bile salt biotransformation. To enable this work, an assay for bile salt hydrolase (BSH) that could work with small quantities of stool samples was necessary. Using a turn-on fluorescence probe, we developed a rapid and inexpensive assay to detect BSH enzyme activity with concentrations as low as 6-25 µM, which is considerably more robust than prior approaches. We successfully applied this rhodamine-based assay to detect BSH activity in a wide range of biological samples such as recombinant protein, whole cells, fecal samples, and gut lumen content from mice. We were able to detect significant BSH activity in small amounts of mouse fecal/gut content (20-50 mg) within 2 h, which illustrates its potential for use in various biological/clinical applications. Using this assay, we investigated the diurnal fluctuations of BSH activity in the large intestine of mice. By using time restricted feeding conditions, we provided direct evidence of 24 h rhythmicity in microbiome BSH activity levels and showed that this rhythmicity is influenced by feeding patterns. Our novel function-centric approach has potential to aid in the discovery of therapeutic, diet, or lifestyle interventions for correction of circadian perturbations linked to bile metabolism.

Development of a Nanoscale Chemical Exchange Saturation Transfer Magnetic Resonance Imaging Contrast Agent That Measures pH.

AcidoCEST MRI can measure the extracellular pH (pHe) of the tumor microenvironment in mouse models of human cancers and in patients who have cancer. However, chemical exchange saturation transfer (CEST) is an insensitive magnetic resonance imaging (MRI) contrast mechanism, requiring a high concentration of small-molecule agent to be delivered to the tumor. Herein, we developed a nanoscale CEST agent that can measure pH using acidoCEST MRI, which may decrease the requirement for high delivery concentrations of agent. We also developed a monomer agent for comparison to the polymer. After optimizing CEST experimental conditions, we determined that the polymer agent could be used during acidoCEST MRI studies at 125-fold and 488-fold lower concentration than the monomer agent and iopamidol, respectively. We also determined that both agents can measure pH with negligible dependence on temperature. However, pH measurements with both agents were dependent on concentration, which may be due to concentration-dependent changes in hydrogen bonding and/or steric hindrance. We performed in vivo acidoCEST MRI studies using the three agents to study a xenograft MDA-MB-231 model of mammary carcinoma. The tumor pHe measurements were 6.33 ± 0.12, 6.70 ± 0.15, and 6.85 ± 0.15 units with iopamidol, the monomer agent, and polymer agent, respectively. The higher pHe measurements with the monomer and polymer agents were attributed to the concentration dependence of these agents. This study demonstrated that nanoscale agents have merit for CEST MRI studies, but consideration should be given to the dependence of CEST contrast on the concentration of these agents.

Simultaneous Evaluations of pH and Enzyme Activity with a CEST MRI Contrast Agent.

The extracellular tumor microenvironment of many solid tumors has high acidosis and high protease activity. Simultaneously assessing both characteristics may improve diagnostic evaluations of aggressive tumors and the effects of anticancer treatments. Noninvasive imaging methods have previously been developed that measure extracellular pH or can detect enzyme activity using chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI). Herein, we developed a single-hybrid CEST agent that can simultaneously measure pH and evaluate protease activity using a combination of dual-power acidoCEST MRI and catalyCEST MRI. Our agent showed CEST signals at 9.2 ppm from a salicylic acid moiety and at 5.0 ppm from an aryl amide. The CEST signal at 9.2 ppm could be measured after selective saturation was applied at 1 and 4 µT, and these measurements could be used with a ratiometric analysis to determine pH. The CEST signal at 5.0 ppm from the aryl amide disappeared after the agent was treated with cathepsin B, while the CEST signal at 9.2 ppm remained, indicating that the agent could detect protease activity through the amide bond cleavage. Michaelis-Menten kinetics studies with catalyCEST MRI demonstrated that the binding affinity (as shown with the Michaelis constant KM), the catalytic turnover rate (kcat), and catalytic efficiency (kcat/KM) were each higher for cathepsin B at lower pH. The kcat rates measured with catalyCEST MRI were lower than the comparable rates measured with liquid chromatography-mass spectrometry (LC-MS), which reflected a limitation of inherently noisy and relatively insensitive CEST MRI analyses. Although this level of precision limited catalyCEST MRI to semiquantitative evaluations, these semiquantitative assessments of high and low protease activity still had value by demonstrating that high acidosis and high protease activity can be used as synergistic, multiparametric biomarkers.