Combining Charge Density Analysis with Machine Learning Tools To Investigate the Cruzain Inhibition Mechanism

LUCHI, ADRIANO M.; VILLAFAÑE, ROXANA N.; GÓMEZ CHÁVEZ, J. LEONARDO; BOGADO, M. LUCRECIA; ANGELINA, EMILIO; PERUCHENA, NELIDA MARIA

ACS Omega

American Chemical Society

Lugar: Washington DC; Año: 2019 vol. 4 p. 19582 - 19594

2470-1343

Trypanosoma cruzi, a flagellate protozoanparasite, is responsible for Chagas disease. The parasitemajor cysteine protease, cruzain (Cz), plays a vital role atevery stage of its life cycle and the active-site region of theenzyme, similar to those of other members of the papainsuperfamily, is well characterized. Taking advantage ofstructural information available in public databases about Czbound to known covalent inhibitors, along with theircorresponding activity annotations, in this work, weperformed a deep analysis of the molecular interactions atthe Cz binding cleft, in order to investigate the enzymeinhibition mechanism. Our toolbox for performing this studyconsisted of the charge density topological analysis of thecomplexes to extract the molecular interactions and machine learning classification models to relate the interactions withbiological activity. More precisely, such a combination was useful for the classification of molecular interactions as ?active-like?or ?inactive-like? according to whether they are prevalent in the most active or less active complexes, respectively. Furtheranalysis of interactions with the help of unsupervised learning tools also allowed the understanding of how these interactionscome into play together to trigger the enzyme into a particular conformational state. Most active inhibitors induce someconformational changes within the enzyme that lead to an overall better fit of the inhibitor into the binding cleft. Curiously,some of these conformational changes can be considered as a hallmark of the substrate recognition event, which means thatmost active inhibitors are likely recognized by the enzyme as if they were its own substrate so that the catalytic machinery isarranged as if it is about to break the substrate scissile bond. Overall, these results contribute to a better understanding of theenzyme inhibition mechanism. Moreover, the information about main interactions extracted through this work is already beingused in our lab to guide docking solutions in ongoing prospective virtual screening campaigns to search for novel noncovalentcruzain inhibitors