Combining Charge Density Analysis with Machine Learning Tools To Investigate the Cruzain Inhibition Mechanism
| Autor: | Nelida Maria Peruchena, Emilio Angelina, J Leonardo Gómez Chávez, Roxana Noelia Villafañe, M Lucrecia Bogado, Adriano Martín Luchi |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
chemistry.chemical_classification
Virtual screening CHARGE DENSITY General Chemical Engineering Biological activity General Chemistry Computational biology QM-QTAIM Cysteine protease Article SVM-RFE purl.org/becyt/ford/1 [https] Scissile bond Papain chemistry.chemical_compound Chemistry Enzyme chemistry Covalent bond Docking (molecular) purl.org/becyt/ford/1.4 [https] STRUCTURE-BASED DRUG DISCOVERY QD1-999 |
| Zdroj: | ACS Omega, Vol 4, Iss 22, Pp 19582-19594 (2019) CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET ACS Omega |
| ISSN: | 2470-1343 |
| Popis: | 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. Fil: Luchi, Adriano Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Villafañe, Roxana Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Gómez Chávez, José Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Bogado, María Lucrecia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Angelina, Emilio Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina Fil: Peruchena, Nelida Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina |
| Databáze: | OpenAIRE |
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