Taosheng Chen

Mass spectrometry (MS)-based proteomics has emerged as a versatile platform in modern drug discovery, enabling system-wide interrogation of protein abundance, posttranslational modifications (PTMs), turnover, structural stability, and interactions with small molecules in native biological contexts. In this review, we introduce recent advances in MS and summarize how scalable, high-throughput technologies support target identification and mechanism-of-action studies. We discuss global profiling of the proteome and PTMs under drug perturbation, as well as proteome-wide stability assays such as thermal proteome profiling and limited proteolysis–MS. We further discuss activity-based protein profiling, fully functionalized and photoaffinity probes, and affinity-matrix approaches that expand covalent and noncovalent ligand discovery. Finally, we highlight multiple proteomics-driven strategies in targeted protein degradation. Together, these integrated MS-based approaches in drug discovery, often termed chemoproteomics, provide a coherent framework for linking chemical perturbations to functional proteome remodeling, expanding the druggable proteome and accelerating therapeutic development.

Sonic Hedgehog (SHH) signaling functions in temporal- and context-dependent manners to pattern diverse tissues during embryogenesis. The signal transducer Smoothened (SMO) is induced by sterols, oxysterols, and arachidonic acid (AA) through binding pockets in its extracellular cysteine-rich domain (CRD) and 7-transmembrane (7TM) bundle. In vitro analyses suggest SMO signaling is allosterically enhanced by combinatorial ligand binding to these pockets, but in vivo evidence of SMO allostery is lacking. Herein, we map an AA binding pocket at the top of the 7TM bundle and show that its disruption attenuates SHH- and sterol-stimulated SMO induction. A knock-in mouse model of compromised AA binding reveals that homozygous mutant mice are cyanotic, exhibit perinatal lethality, and display congenital heart disease. Surviving mutants demonstrate pulmonary maldevelopment and fail to thrive. Neurodevelopment is unaltered in these mice, suggesting that context-specific allosteric regulation of SMO signaling allows for precise tuning of pathway activity during cardiopulmonary development.

Rifamycins are a cornerstone of antimycobacterial therapy. However, their clinical use is limited by drug-drug interactions, arising from activation of the nuclear receptor pregnane X receptor (PXR). PXR activation induces expression of drug-metabolizing enzymes, including the cytochrome P450 3A4 isoform (CYP3A4), and accelerates clearance of coadministered medications. The structural understanding of rifamycin-PXR interactions remains limited. We designed a series of C25-modified rifabutin analogs and systematically evaluated their PXR binding, transcriptional activation, and pharmacological profile. Several analogs retained PXR binding affinity yet showed reduced CYP3A4 induction, exhibiting behavior consistent with antagonists or inverse agonists and revealing a disconnect between receptor binding and transcriptional activation. Molecular dynamics simulations indicated C25 modifications may disrupt positioning of the PXR α12 helix through steric interactions. These findings demonstrate conservative modifications of rifamycin can potentially convert PXR agonists into antagonists or inverse agonists and offer a structure-guided framework for developing rifamycins with attenuated CYP3A4 induction.

Label-free high-throughput screening (HTS) technologies have greatly accelerated early drug discovery by allowing direct measurement of biochemical activity without the need for chemical labels. Techniques like MALDI-TOF, RapidFire, desorption electrospray ionization, and acoustic-mist ionization mass spectrometry have shown strong analytical performance. More recently, acoustic droplet ejection mass spectrometry (AEMS) has emerged as a next-generation platform capable of analyzing over a thousand data points per hour. In this study, we thoroughly evaluated an AEMS workflow using cytochrome P450 3A4 (CYP3A4) as a model enzyme. Screening a library of 9702 compounds, this work represents the first successful label-free HTS campaign for CYP3A4 inhibitors in a 384-well format using AEMS. The result using nifedipine as a substrate was comparable to the widely used P450-Glo luminescence assay but with fewer inhibitor hits identifications. To further compare and parse differences between the methods and the substrates, 819 hits were retested using AEMS with luciferin isopropyl acetal, the same substrate used in the luminescence assay. The findings confirmed that both the assay method and the substrate influence which inhibitors were detected. Overall, this study established AEMS as a robust and reliable tool for CYP3A4 inhibitor screening. It offers high analytical precision, fewer false positives, compatibility with multiple substrates, and direct detection of metabolites, providing a practical way to validate hits and improve confidence in early drug discovery efforts.

Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood–brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis), a systems biology framework that predicts synergistic, BBB-permeable drug combinations by identifying hidden drivers that sustain oncogenic programs without detectable genetic or transcriptional alterations. Integrating MB-specific gene networks, transcriptomic data, and drug–gene interactions, SINBA prioritized 32 candidate combinations, of which 19 were experimentally validated as synergistic. The MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in human xenograft and immunocompetent mouse models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of the developmental origins of G3 MB, accompanied by immune microenvironment reprogramming. These findings establish hidden-driver inference as a generalizable framework for rational drug combination discovery.