In Silico prediction of antibacterial activity of sesquiterpene lactones using density-functional theory and quantitative structure-activity relationship methods

The growing resistance developed by bacteria to antibiotics is a problem that involves every social stratum. Therefore, the development of new and effective anti-bacterial components is of vital importance for our society. Sesquiterpene Lactones (STL) are a group of secondary metabolites isolated from plants that have shown a wide spectrum of biological activities especially antibacterial activity against methicillin-resistant staphylococcus aureus (MRSA). Unfortunately, the experimental methods to study the effectiveness of plant-based antibiotics are expensive and time-consuming. In order to tackle these limitations in silico studies can be applied to accelerate the development of more efficient antibiotics. In this study, electronic structure calculations on 21 STL were performed to develop a model capable to predicting the antibacterial activity of new STL molecules. By using an optimal combination of density-functional tight-binding (DFTB) method and ab initio densityfunctional theory (DFT) calculations, we were able to calculate the most energetically favorable conformers, their atomic structure and physical-chemical properties. The quantum mechanically computed values were them combined using Quantitative Structure-Activity Relationship models considering experimental antibacterial activity. The developed QSAR model used different combinations of two descriptors. Preliminary results suggest that models that includes the HOMO and electronic energy correlates better the antibacterial activity. These results could allow reliable prediction of antibacterial activity for new compounds that belong to the STL family based on the DFT computed properties.

• Main Author: Puga Montesdeoca, Fabián Aníbal (author)
• Format: bachelorThesis
• Language: eng
• Published: 2020
• Subjects: Conformers; DFT; DFTB; Methicillin-Resistant Staphylococcus Aureus; xTB; CREST; ORCA; Descriptors
• Online Access: http://repositorio.yachaytech.edu.ec/handle/123456789/291