Back

Functional Mapping of the Trypanosoma cruzi Serinome by Fluorophosphonate Activity-Based Protein Profiling

Isern, J. A.; Mediavilla, M. G.; Porta, E. O. J.; Merli, M. L.; Ballari, M. S.; Cricco, J. A.; Labadie, G. R.; Steel, P. G.

2026-07-20 biochemistry
10.64898/2026.07.18.739127 bioRxiv
Show abstract

Serine hydrolases (SHs) constitute one of the largest enzyme superfamilies in eukaryotes, yet their roles in Trypanosoma cruzi, the causative agent of Chagas disease, remain largely uncharacterized. Here, we report an activity-based chemoproteomic map of the T. cruzi epimastigote serinome by combining genome-informed in silico curation with whole-cell activity-based protein profiling (ABPP) using a panel of cell-permeable fluorophosphonate (FP)-alkyne probes. Whole-cell labelling followed by label-free quantitative proteomics (LFQ-MS), identified 37 enriched SH-like proteins, including 35 with conserved or partially conserved catalytic triad/dyad features, spanning lipases, peptidases, esterases, and previously uncharacterized hydrolases. The 35 SHs represent approximately 63 % of the 56 predicted SHs retained after catalytic-site curation. Domain architecture analysis revealed broad structural diversity, while orthologue-based localization data suggested association with multiple subcellular compartments, including glycosomal, mitochondrial, and endosomal localizations. Gene Ontology enrichment highlighted lipid metabolic and catabolic processes as dominant functional themes, and protein-protein interaction network analysis supported functional connectivity among the captured enzymes. Several identified SHs, including oligopeptidase B, prolyl oligopeptidase Tc80, serine carboxypeptidase CPB1, and phospholipase A1 (PLA1) have previously been characterized in trypanosomatids as virulence factors and as mediator of host-pathogen interactions. Together, these findings establish a fluorophosphonate-based chemoproteomic resource for the kinetoplastid community and prioritize probe-accessible active T. cruzi SHs for future functional validation and antiparasitic inhibitor discovery.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.