The Use of AlphaFold for in silico exploration of drug targets in the parasite Trypanosoma cruzi

Abstract

Chagas disease is a devastating neglected disease caused by the parasite Trypanosoma cruzi, which affects millions of people worldwide. The two anti-parasitic drugs available, nifurtimox and benznidazole, have a good efficacy against the acute stage of the infection. But this is short, usually asymptomatic and often goes undiagnosed. Access to treatment is mostly achieved during the chronic stage, when the cardiac and/or digestive life-threatening symptoms manifest. Then, the efficacy of both drugs is diminished, and their long administration regimens involve frequently associated adverse effects that compromise treatment compliance. Therefore, the discovery of safer and more effective drugs is an urgent need. Despite its advantages over lately used phenotypic screening, target-based identification of new anti-parasitic molecules has been hampered by incomplete annotation and lack of structures of the parasite protein space. Presently, the AlphaFold Protein Structure Database is home to 19,036 protein models from T. cruzi, which could hold the key to not only describe new therapeutic approaches, but also shed light on molecular mechanisms of action for known compounds. In this proof-of-concept study, we screened the AlphaFold T. cruzi set of predicted protein models to find prospective targets for a pre-selected list of compounds with known anti-trypanosomal activity using docking-based inverse virtual screening. The best receptors (targets) for the most promising ligands were analyzed in detail to address molecular interactions and potential drugs' mode of action. The results provide insight into the mechanisms of action of the compounds and their targets, and pave the way for new strategies to finding novel compounds or optimize already existing ones.