STRUCTURAL MODELING AND SELECTION OF POTENTIAL INHIBITORS FOR THE TRYPANOSOMA CRUZI PROTEIN KINASE TCMK2

Trypanosoma cruzi protein kinase TcMK2 was identified through genomic analysis of the parasite, and its three-dimensional structure remains uncharacterized. Parasites in which the TcMK2-encoding gene has been knocked out fail to complete metacyclogenesis, the differentiation step in which epimastigotes become metacyclic trypomastigotes, indicating that TcMK2 is a potential target for the design of enzyme inhibitors against Chagas disease. This project aims to model the TcMK2 structure and identify candidate inhibitors using in silico approaches; inhibition of this enzyme can also be used to probe its importance in other stages of the parasite life cycle, in addition to compromising parasite viability.

The TcMK2 structure was modeled with AlphaFold 3 server, which predicts protein structure from amino acid sequence using artificial intelligence trained on protein structure databases. The resulting model was subjected to a 1 µs molecular dynamics (MD) simulation, and Root Mean Square Deviation (RMSD) and Radius of Gyration (RG) analyses were used to select the most sampled conformation. Using this structure, molecular docking of TcMK2 with candidate inhibitors retrieved from virtual screening of the ZINC database is being performed with AutoDock Vina.

An initial full-length TcMK2 model (pTM = 0.64) showed an extensive, low-confidence unfolded region outside the active site. Since docking will target only the active site, a second model restricted to this region was generated, yielding higher confidence (pTM = 0.78) and used for the MD simulation. Conformational statistics obtained from three-dimensional RG-versus-RMSD-versus-free-energy plots allowed identification of the most probable conformation, which is currently being used in virtual screening for inhibitor candidates.

The TcMK2 amino acid sequence shares less than 30% identity with proteins of known secondary structure, which hinders full-length modeling by AlphaFold. However, the enzyme's CAMK domain is highly conserved, allowing this region to be modeled with high confidence. Since this domain comprises the enzyme's active site, the model is suitable for molecular docking and the search for potential TcMK2 inhibitors.

This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).