Abstract
Chagas’s is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi. According to the World Health Organization, 7 million people are infected worldwide leading to 7000 deaths per year. Drugs available, nifurtimox and benzimidazole, are limited due to low efficacy and high toxicity. As a validated target, cruzain represents a major front in drug discovery attempts for Chagas disease. Herein, we describe the development of 2D QSAR (\(r_{{{\text{pred}}}}^{2}\) = 0.81) and a 3D-QSAR-based pharmacophore (\(r_{{{\text{pred}}}}^{2}\) = 0.82) from a series of non-covalent cruzain inhibitors represented mostly by oxadiazoles (lead compound, IC50 = 200 nM). Both models allowed us to map key intermolecular interactions in S1′, S2 and S3 cruzain sub-sites (including halogen bond and C‒H/π). To probe the predictive capacity of obtained models, inhibitors available in the literature from different classes displaying a range of scaffolds were evaluate achieving mean absolute deviation of 0.33 and 0.51 for 2D and 3D models, respectively. CoMFA revealed an unexplored region where addition of bulky substituents to produce new compounds in the series could be beneficial to improve biological activity.
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Abbreviations
- AutoQSAR:
-
Automated QSAR
- BSSE:
-
Basis set superposition error
- B3LYP-D3:
-
Becke, three-parameter hybrid functional combined with Lee–Yang–Parr correlation functional including Grimme’s D3 dispersion scheme
- CoMFA:
-
Comparative molecular field analysis
- GA:
-
Genetic algorithm
- GALAHAD:
-
Genetic algorithm with linear assignment for the hypermolecular alignment of datasets
- HTS:
-
High-throughput screening
- KPLS:
-
Kernel-based partial least square
- LMP2:
-
Localized Møller–Plesset perturbation theory
- MLR:
-
Multiple linear regression
- OPLS3:
-
Optimized potentials for liquid simulations
- PCR:
-
Principal component analysis
- pIC50 :
-
Logarithm of the inverse of compound concentration that reduces the enzyme activity by 50% [log (1/IC50)]
- PLS:
-
Partial least square
- q 2 :
-
Leave-one-out cross-validated correlation coefficient
- QSAR:
-
Quantitative structure–activity relationship
- r 2 :
-
Non-cross-validated correlation coefficient
- RMSE:
-
Root-mean-square error of test set predictions
- SAR:
-
Structure–activity relationship
- SD:
-
Standard deviation
- SDC:
-
Standard deviation coefficient
- SEE:
-
Standard error of estimate
- SEP:
-
Standard error prediction
- X3LYP:
-
Extended hybrid functional combined with Lee–Yang–Parr correlation functional
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Funding
Financial support was provided by the State of São Paulo Research Foundation (FAPESP, Fundação de Amparo à Pesquisa do Estado de São Paulo), Grant 2013/07600-3. A.S.S. and M.T.O acknowledge the Coordination for the Improvement of Higher Education Personnel (CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for institutional grants.
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de Souza, A.S., de Oliveira, M.T. & Andricopulo, A.D. Development of a pharmacophore for cruzain using oxadiazoles as virtual molecular probes: quantitative structure–activity relationship studies. J Comput Aided Mol Des 31, 801–816 (2017). https://doi.org/10.1007/s10822-017-0039-0
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DOI: https://doi.org/10.1007/s10822-017-0039-0