Molecular modelling and competitive inhibition of a Mycobacterium tuberculosis multidrug-resistance efflux pump.

Tuberculosis is a major cause of mortality and morbidity in developing countries, and the emergency of multidrug and extensive drug resistance cases is an utmost issue on the control of the disease. Despite the efforts on the development of new antibiotics, eventually there will be strains resistant to them as well. Efflux plays an important role in the evolution of resistance in Mycobacterium tuberculosis. Tap is an important efflux pump associated with tuberculosis resistant to isoniazid, rifampicine and ofloxacin and with multidrug resistance. The development of efflux inhibitors for Tap could raise the effectiveness of second line drugs and reduce the duration of the current treatment. Therefore the objective of this study is to build a reliable molecular model of Tap efflux pump and test the possible competitive inhibition between efflux inhibitors and antibiotics in the optimized structure. We built twenty five Tap models with molecular modelling to elect the best according to the results of the validation analysis. The elected model went through to a 100 ns molecular dynamics simulation in a lipid bilayer, and the resulting optimized structure was used in docking studies to test if the used efflux inhibitors may act via competitive inhibition on antibiotics. The validation results pointed the model built by Phyre2 as the closest to a possible native Tap structure, and therefore it was the elected model. RSMD analysis revealed the model is stable, where the predicted binding site stabilized between 15 and 20 ns, maintaining the RMSD at around 0.35 Šthroughout the molecular dynamics simulation in a lipid bilayer. Therefore this model is reliable and can also be used for further studies. The docking studies showed a possibility of competitive inhibition by NUNL02 on ofloxacin and bedaquiline, and by verapamil on ofloxacin and rifampicin. This presents the possibility that NUNL02 and verapamil are possible inhibitors of Tap efflux and highlights the importance of including efflux inhibitors as adjuvants to the tuberculosis therapy, as it indicates a possible extrusion of ofloxacin, rifampicin and bedaquilin by Tap.

In vitro and in silico analysis of the efficiency of tetrahydropyridines as drug efflux inhibitors in Escherichia coli.

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.

An infrastructure to mine molecular descriptors for ligand selection on virtual screening.

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.