End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution.

Photoaffinity labeling (PAL) methodologies have proven to be instrumental for the unbiased deconvolution of protein-ligand binding events in physiologically relevant systems. However, like other chemical proteomic workflows, they are limited in many ways by time-intensive sample manipulations and data acquisition techniques. Here, we describe an approach to address this challenge through the innovation of a carboxylate bead-based protein cleanup procedure to remove excess small-molecule contaminants and couple it to plate-based, proteomic sample processing as a semiautomated solution.

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 M that preserve the core scaffold.

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.

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 (M), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates.

Profiling How the Gut Microbiome Modulates Host Xenobiotic Metabolism in Response to Benzo[]pyrene and 1-Nitropyrene Exposure.

The gut microbiome is a key contributor to xenobiotic metabolism. Polycyclic aromatic hydrocarbons (PAHs) are an abundant class of environmental contaminants that have varying levels of carcinogenicity depending on their individual structures. Little is known about how the gut microbiome affects the rates of PAH metabolism.