Molecular graphics approach to bacterial AcrB protein–β-lactam antibiotic molecular recognition in drug efflux mechanism

Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure-Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q(2)=0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account--something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path toward further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins.

4D-QSAR analysis and pharmacophore modeling: electron conformational-genetic algorithm approach for penicillins

4D-QSAR studies were performed on a series of 87 penicillin analogues using the electron conformational-genetic algorithm (EC-GA) method. In this EC-based method, each conformation of the molecular system is described by a matrix (ECMC) with both electron structural parameters and interatomic distances as matrix elements. Multiple comparisons of these matrices within given tolerances for high active and low active penicillin compounds allow one to separate a smaller number of matrix elements (ECSA) which represent the pharmacophore groups. The effect of conformations was investigated building model 1 and 2 based on ensemble of conformers and single conformer, respectively. GA was used to select the most important descriptors and to predict the theoretical activity of the training (74 compounds) and test (13 compounds, commercial penicillins) sets. The model 1 for training and test sets obtained by optimum 12 parameters gave more satisfactory results (R(training)(2)=0.861, SE(training)=0.044, R(test)(2)=0.892, SE(test)=0.099, q(2)=0.702, q(ext1)(2)=0.777 and q(ext2)(2)=0.733) than model 2 (R(training)(2)=0.774, SE(training)=0.056, R(test)(2)=0.840, SE(test)=0.121, q(2)=0.514, q(ext1)(2)=0.641 and q(ext2)(2)=0.570). To estimate the individual influence of each of the molecular descriptors on biological activity, the E statistics technique was applied to the derived EC-GA model.

Chemometric analysis of the multidrug resistance in strains of Penicillium digitatum

Multidrug resistance activities pECr50 of diverse strains of pathogenic fungus Penicillium digitatum against seven toxicants were studied by Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Fungal growth data (radii, circumferences, surface areas of fungal colonies, radius differences and ratios) in absence and presence of toxicants were used to derive eight new descriptors for 35 fungal strains. This data set was studied by PCA and HCA, and was correlated with the genome descriptor PCR for expression of gene CYP51 by Partial Least Squares (PLS) regression. Both analyses of pECr50 data and of fungal growth data have identified baseline resistance character, origin and target fruits of the fungal strains. In addition, the analysis of fungal growth data shows that fungal growth morphology is multivariate by nature, meaning that experimental data can be explored more intensely than in usual practice. Fungal growth is directly related to the production of enzyme P45014DM as the main resistance mechanism of P. digitatum against demethylation inhibitors. This is visible from a parsimonious PLS model (two principal components, Q2 = 0.985, R2 = 0.991, SEV = 0.028), validated with eight strains in the external validation set (Q2ev = 0.982, R2ev = 0.990, SECev = 0.025). Chemometric methods in exploring bioassay data are promising approaches to obtain useful information on fungal resistance and to apply these findings in practice.