Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism
Trypanosoma cruzi, a flagellate protozoan parasite, is responsible for Chagas disease. The parasite major cysteine protease, cruzain (Cz), plays a vital role at every stage of its life cycle and the active-site region of the enzyme, similar to those of other members of the papain superfamily, is wel...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
American Chemical Society
2025
|
| Materias: | |
| Acceso en línea: | http://repositorio.unne.edu.ar/handle/123456789/56524 |
| Aporte de: |
| id |
I48-R184-123456789-56524 |
|---|---|
| record_format |
dspace |
| spelling |
I48-R184-123456789-565242025-04-25T13:35:09Z Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism Luchi, Adriano Martín Villafañe, Roxana Noelia Gómez Chávez, José Leonardo Bogado, María Lucrecia Angelina, Emilio Luis Peruchena, Nélida María Trypanosoma cruzi Chagas disease Parasite Trypanosoma cruzi, a flagellate protozoan parasite, is responsible for Chagas disease. The parasite major cysteine protease, cruzain (Cz), plays a vital role at every stage of its life cycle and the active-site region of the enzyme, similar to those of other members of the papain superfamily, is well characterized. Taking advantage of structural information available in public databases about Cz bound to known covalent inhibitors, along with their corresponding activity annotations, in this work, we performed a deep analysis of the molecular interactions at the Cz binding cleft, in order to investigate the enzyme inhibition mechanism. Our toolbox for performing this study consisted of the charge density topological analysis of the complexes to extract the molecular interactions and machine learning classification models to relate the interactions with biological 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. Further analysis of interactions with the help of unsupervised learning tools also allowed the understanding of how these interactions come into play together to trigger the enzyme into a particular conformational state. Most active inhibitors induce some conformational 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 that most active inhibitors are likely recognized by the enzyme as if they were its own substrate so that the catalytic machinery is arranged as if it is about to break the substrate scissile bond. Overall, these results contribute to a better understanding of the enzyme inhibition mechanism. Moreover, the information about main interactions extracted through this work is already being used in our lab to guide docking solutions in ongoing prospective virtual screening campaigns to search for novel noncovalent cruzain inhibitors. 2025-04-16T14:14:58Z 2025-04-16T14:14:58Z 2019 Artículo Luchi, Adriano Martín, et al., 2019. Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism. ACS Omega. Washington: American Chemical Society, vol. 4, no. 22, p. 19582−19594. ISSN 2470-1343. 2470-1343 http://repositorio.unne.edu.ar/handle/123456789/56524 eng https://pubs.acs.org/doi/epdf/10.1021/acsomega.9b01934?ref=article_openPDF openAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf p. 19582−19594 application/pdf American Chemical Society ACS Omega, 2019, vol. 4, no. 22, p. 19582−19594. |
| institution |
Universidad Nacional del Nordeste |
| institution_str |
I-48 |
| repository_str |
R-184 |
| collection |
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) |
| language |
Inglés |
| topic |
Trypanosoma cruzi Chagas disease Parasite |
| spellingShingle |
Trypanosoma cruzi Chagas disease Parasite Luchi, Adriano Martín Villafañe, Roxana Noelia Gómez Chávez, José Leonardo Bogado, María Lucrecia Angelina, Emilio Luis Peruchena, Nélida María Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism |
| topic_facet |
Trypanosoma cruzi Chagas disease Parasite |
| description |
Trypanosoma cruzi, a flagellate protozoan parasite, is responsible for Chagas disease. The parasite major cysteine protease, cruzain (Cz), plays a vital role at every stage of its life cycle and the active-site region of the enzyme, similar to those of other members of the papain superfamily, is well characterized. Taking advantage of structural information available in public databases about Cz bound to known covalent inhibitors, along with their corresponding activity annotations, in this work, we performed a deep analysis of the molecular interactions at the Cz binding cleft, in order to investigate the enzyme inhibition mechanism. Our toolbox for performing this study consisted of the charge density topological analysis of the complexes to extract the molecular interactions and machine learning classification models to relate the interactions with biological 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. Further analysis of interactions with the help of unsupervised learning tools also allowed the understanding of how these interactions come into play together to trigger the enzyme into a particular conformational state. Most active inhibitors induce some conformational 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 that most active inhibitors are likely recognized by the enzyme as if they were its own substrate so that the catalytic machinery is arranged as if it is about to break the substrate scissile bond. Overall, these results contribute to a better understanding of the enzyme inhibition mechanism. Moreover, the information about main interactions extracted through this work is already being used in our lab to guide docking solutions in ongoing prospective virtual screening campaigns to search for novel noncovalent cruzain inhibitors. |
| format |
Artículo |
| author |
Luchi, Adriano Martín Villafañe, Roxana Noelia Gómez Chávez, José Leonardo Bogado, María Lucrecia Angelina, Emilio Luis Peruchena, Nélida María |
| author_facet |
Luchi, Adriano Martín Villafañe, Roxana Noelia Gómez Chávez, José Leonardo Bogado, María Lucrecia Angelina, Emilio Luis Peruchena, Nélida María |
| author_sort |
Luchi, Adriano Martín |
| title |
Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism |
| title_short |
Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism |
| title_full |
Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism |
| title_fullStr |
Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism |
| title_full_unstemmed |
Combining charge density analysis with machine learning tools to investigate the Cruzain inhibition mechanism |
| title_sort |
combining charge density analysis with machine learning tools to investigate the cruzain inhibition mechanism |
| publisher |
American Chemical Society |
| publishDate |
2025 |
| url |
http://repositorio.unne.edu.ar/handle/123456789/56524 |
| work_keys_str_mv |
AT luchiadrianomartin combiningchargedensityanalysiswithmachinelearningtoolstoinvestigatethecruzaininhibitionmechanism AT villafaneroxananoelia combiningchargedensityanalysiswithmachinelearningtoolstoinvestigatethecruzaininhibitionmechanism AT gomezchavezjoseleonardo combiningchargedensityanalysiswithmachinelearningtoolstoinvestigatethecruzaininhibitionmechanism AT bogadomarialucrecia combiningchargedensityanalysiswithmachinelearningtoolstoinvestigatethecruzaininhibitionmechanism AT angelinaemilioluis combiningchargedensityanalysiswithmachinelearningtoolstoinvestigatethecruzaininhibitionmechanism AT peruchenanelidamaria combiningchargedensityanalysiswithmachinelearningtoolstoinvestigatethecruzaininhibitionmechanism |
| _version_ |
1835151121271750656 |