Abstract
Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r2 = 0.98 and q2 = 0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.
Keywords: Human African trypanosomiasis, aldolase, molecular modeling, QSAR
Current Computer-Aided Drug Design
Title:Structure- and Ligand-Based Structure-Activity Relationships for a Series of Inhibitors of Aldolase
Volume: 8 Issue: 4
Author(s): Leonardo G. Ferreira and Adriano D. Andricopulo
Affiliation:
Keywords: Human African trypanosomiasis, aldolase, molecular modeling, QSAR
Abstract: Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r2 = 0.98 and q2 = 0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.
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Cite this article as:
G. Ferreira Leonardo and D. Andricopulo Adriano, Structure- and Ligand-Based Structure-Activity Relationships for a Series of Inhibitors of Aldolase, Current Computer-Aided Drug Design 2012; 8 (4) . https://dx.doi.org/10.2174/157340912803519589
DOI https://dx.doi.org/10.2174/157340912803519589 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
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