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.

State-of-the-Art in the Drug Discovery Pathway for Chagas Disease: A Framework for Drug Development and Target Validation

“…An ideal drug target should be essential in pathologically relevant parasite life stages; sufficiently different from related mammalian proteins to be selective; and druggable, ie presenting well-defined domains expected to bind certain types of compounds. 74 Large-scale evaluation of these characteristics can greatly reduce the number of potential candidates, 75 , 76 prioritizing proteins that are conserved across different DTUs, but not in mammals, 77 and stably expressed in amastigote and trypomastigote stages. Drug targets that are evolutionarily conserved among different parasite strains but differ from the human proteome are expected to have wider clinical efficacy, while minimizing off-target effects.…”

“… 89 AlphaFold models of targets of interest can be easily incorporated into drug discovery pipelines by using them, for instance, in in silico docking experiments, where the binding affinity between small molecules and potential targets is predicted, or in inverse virtual screening experiments to determine the mode of action of prospective drugs. 75 …”

Predicting the duration of action of β2‐adrenergic receptor agonists: Ligand and structure‐based approaches

“…With the release of AF [18], deep learning models have become the go to approaches for the prediction of protein structure. AF-generated structures have already been used to study protein-ligand interactions [28][29][30], and their use for virtual screening has been benchmarked. The active state structure of ADRB2 was generated using AlphaFold2-Multistate by Heo and Feig [19].…”

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

“…The limited use of docking simulations in target identification can be explained by the greater complexity of these calculations, which are markedly more time-consuming than simple similarity searches. Nevertheless, the recent advancements in high-performing computing for docking simulations as well as the availability of optimized docking engines render these simulations amenable for target prediction, at least in well-defined contexts [ 19 ]. In this scenario, one can appreciate the various methods recently proposed for the so-called inverse docking methodology, in which a single molecule is docked into a set of therapeutically relevant proteins to reveal the potential targets and then the related mechanisms of action [ 20 , 21 ].…”