RESUMO
Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.
Assuntos
Antiprotozoários , Leishmania , Leishmaniose , Humanos , Simulação de Acoplamento Molecular , Leishmania/metabolismo , NADH NADPH Oxirredutases/metabolismo , Leishmaniose/parasitologia , Antiprotozoários/uso terapêuticoRESUMO
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.