RESUMO
The objectives of this study were to evaluate tetrahydropyridine derivatives as efflux inhibitors and to understand the mechanism of action of the compounds by in silico studies. Minimum inhibitory concentration (MIC) determination, fluorometric methods and docking simulations were performed. The compounds NUNL02, NUNL09 and NUNL10 inhibited efflux, and NUNL02 is very likely a substrate of the transporter protein AcrB. Docking studies suggested that the mechanism of action could be by competition with substrate for binding sites and protein residues. We showed for the first time the potential of tetrahydropyridines as efflux inhibitors and highlighted compound NUNL02 as an AcrB-specific inhibitor. Docking studies suggested that competition is the putative mechanism of action of these compounds.
Assuntos
Antibacterianos/metabolismo , Transporte Biológico Ativo/efeitos dos fármacos , Inibidores Enzimáticos/metabolismo , Proteínas de Escherichia coli/metabolismo , Escherichia coli/efeitos dos fármacos , Proteínas Associadas à Resistência a Múltiplos Medicamentos/metabolismo , Piridinas/metabolismo , Antibacterianos/química , Inibidores Enzimáticos/química , Proteínas de Escherichia coli/química , Testes de Sensibilidade Microbiana , Simulação de Acoplamento Molecular , Proteínas Associadas à Resistência a Múltiplos Medicamentos/química , Ligação Proteica , Piridinas/químicaRESUMO
The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.