Chemoproteomic Profiling of a Pharmacophore-Focused Chemical Library

Probing the metalloproteome: an 8-mercaptoquinoline motif enriches minichromosome maintenance complex components as significant metalloprotein targets in live cells

Affinity-based probes are valuable tools for detecting binding interactions between small molecules and proteins in complex biological environments. Metalloproteins are a class of therapeutically significant biomolecules which bind metal ions as part of key structural or catalytic domains and are compelling targets for study. However, there is currently a limited range of chemical tools suitable for profiling the metalloproteome. Here, we describe the preparation and application of a novel, photoactivatable affinity-based probe for detection of a subset of previously challenging to engage metalloproteins. The probe, bearing an 8-mercaptoquinoline metal chelator, was anticipated to engage several zinc metalloproteins, including the 26S-proteasome subunit Rpn11. Upon translation of the labelling experiment to mammalian cell lysate and live cell experiments, proteomic analysis revealed that several metalloproteins were competitively enriched. The diazirine probe SMK-24 was found to effectively enrich multiple components of the minichromosome maintenance complex, a zinc metalloprotein assembly with helicase activity essential to DNA replication. Cell cycle analysis experiments revealed that HEK293 cells treated with SMK-24 experienced stalling in G0/G1 phase, consistent with inactivation of the DNA helicase complex. This work represents an important contribution to the library of cell-permeable chemical tools for studying a collection of metalloproteins for which no previous probe existed.

Chemoproteomic Identification of Spermidine-Binding Proteins and Antitumor-Immunity Activators

Cancer immune therapies, particularly programmed cell death protein 1 (PD-1) blockade immunotherapy, falter in aged individuals due to compromised T-cell immunity. Spermidine, a biogenic polyamine that declines along with aging, shows promise in restoring antitumor immunity by enhancing mitochondrial fatty acid oxidation (FAO). Herein, we report a spermidine-based chemoproteomic probe (probe 2) that enables profiling of spermidine-binding proteins and screening for small-molecule enhancers of mitochondrial FAO. Chemoproteomic profiling by the probe revealed 140 proteins engaged in cellular interaction with spermidine, with a significant majority being mitochondrial proteins. Hydroxyl coenzyme A (CoA) dehydrogenase subunits α (HADHA) and other lipid metabolism-linked proteins are among the mitochondrial proteins that have attracted considerable interest. Screening spermidine analogs with the probe led to the discovery of compound 13, which interacts with these lipid metabolism-linked proteins and activates HADHA. This simple and biostable synthetic compound we named "spermimic" mirrors spermidine's ability to enhance mitochondrial bioenergetics and displays similar effectiveness in augmenting PD-1 blockade therapy in mice. This study lays the foundation for developing small-molecule activators of antitumor immunity, offering potential in combination cancer immunotherapy.

Leveraging histone glycation for cancer diagnostics and therapeutics

Cancer cells undergo metabolic reprogramming to rely mostly on aerobic glycolysis (the Warburg effect). The increased glycolytic intake enhances the intracellular levels of reactive sugars and sugar metabolites. These reactive species can covalently modify macromolecules in a process termed glycation. Histones are particularly susceptible to glycation, resulting in substantial alterations to chromatin structure, function, and transcriptional output. Growing evidence suggests a link between dysregulated metabolism of tumors and cancer proliferation through epigenetic changes. This review discusses recent advances in the understanding of histone glycation, its impact on the epigenetic landscape and cellular fate, and its role in cancer. In addition, we investigate the possibility of using histone glycation as biomarkers and targets for anticancer therapeutics.

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints

The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.