Molecular description of α‐keto‐based inhibitors of cruzain with activity against Chagas disease combining 3D‐QSAR studies and molecular dynamics. (18th May 2018) - Saraiva, Ádria P. B.; Miranda, Ricardo M.; Valente, Renan P. P.; Araújo, Jéssica O.; Souza, Rutelene N. B.; Costa, Clauber H. S.; Oliveira, Amanda R. S.; Almeida, Michell O.; Figueiredo, Antonio F.; Ferreira, João E. V.; Alves, Cláudio Nahum; Honorio, Kathia M.
Abstract : In this work, a group of α–keto‐based inhibitors of the cruzain enzyme with anti‐chagas activity was selected for a three‐dimensional quantitative structure–activity relationship study (3D‐QSAR) combined with molecular dynamics (MD). Firstly, statistical models based on Partial Least Square (PLS) regression were developed employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) descriptors. Validation parameters ( q 2 and r 2 )for the models were, respectively, 0.910 and 0.997 (CoMFA) and 0.913 and 0.992 (CoMSIA). In addition, external validation for the models using a test group revealed r 2 pred  = 0.728 (CoMFA) and 0.971 (CoMSIA). The most relevant aspect in this study was the generation of molecular fields in both favorable and unfavorable regions based on the models developed. These fields are important to interpret modifications necessary to enhance the biological activities of the inhibitors. This analysis was restricted considering the inhibitors in a fixed conformation, not interacting with their target, the cruzain enzyme. Then, MD was employed taking into account important variables such as time and temperature. MD helped describe the behavior of the inhibitors and their properties showed similar results as those generated by QSAR‐3D study. Abstract : α–keto‐based inhibitors of the cruzain enzyme with anti‐chagas activity was selected for a three‐dimensional quantitative structure‐activity relationship study (3D‐QSAR) combined with molecular dynamics (MD). Partial Least Square (PLS) regression were developed employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) descriptors. Then MD was employed taking into account important variables such as time and temperature. MD helped describe the behavior of the inhibitors and their properties showed similar results as those generated by QSAR‐3D study.