RESUMEN
The accurate description of large molecular systems has triggered the development of new computational methods. Due to the computational cost of modeling large systems, the methods usually require a trade-off between accuracy and speed. Therefore, benchmarking to test the accuracy and precision of the method is an important step in their development. The typical gold standard for evaluating these methods is isolated molecules, because of the low computational cost. However, the advent of high-performance computing has made it possible to benchmark computational methods using observables from more complex systems such as liquid solutions. To this end, infrared spectroscopy provides a suitable set of observables (i.e., vibrational transitions) for liquid systems. Here, IR spectroscopy observables are used to benchmark the predictions of the newly developed GFN2-xTB semiempirical method. Three different IR probes (i.e., N-methylacetamide, benzonitrile, and semiheavy water) in solution are selected for this purpose. The work presented here shows that GFN2-xTB predicts central frequencies with errors of less than 10% in all probes. In addition, the method captures detailed properties of the molecular environment such as weak interactions. Finally, the GFN2-xTB correctly assesses the vibrational solvatochromism for N-methylacetamide and semiheavy water but does not have the accuracy needed to properly describe benzonitrile. Overall, the results indicate not only that GFN2-xTB can be used to predict the central frequencies and their dependence on the molecular environment with reasonable accuracy but also that IR spectroscopy data of liquid solutions provide a suitable set of observables for the benchmarking of computational methods.
RESUMEN
Bacterial resistance is responsible for a wide variety of health problems, both in children and adults. The persistence of symptoms and infections are mainly treated with ß-lactam antibiotics. The increasing resistance to those antibiotics by bacterial pathogens generated the emergence of extended-spectrum ß-lactamases (ESBLs), an actual public health problem. This is due to rapid mutations of bacteria when exposed to antibiotics. In this case, ß-lactamases are enzymes used by bacteria to hydrolyze the beta-lactam rings present in the antibiotics. Therefore, it was necessary to explore novel molecules as potential ß-lactamases inhibitors to find antibacterial compounds against infection caused by ESBLs. A computational methodology based on molecular docking and molecular dynamic simulations was used to find new microalgae metabolites inhibitors of ß-lactamase. Six 3D ß-lactamase proteins were selected, and the molecular docking revealed that the metabolites belonging to the same structural families, such as phenylacridine (4-Ph), quercetin (Qn), and cryptophycin (Cryp), exhibit a better binding score and binding energy than commercial clinical medicine ß-lactamase inhibitors, such as clavulanic acid, sulbactam, and tazobactam. These results indicate that 4-Ph, Qn, and Cryp molecules, homologous from microalgae metabolites, could be used, likely as novel ß-lactamase inhibitors or as structural templates for new in-silico pharmaceutical designs, with the possibility of combatting ß-lactam resistance.
