Juxtaposing the Reactivity Descriptors of Plastic Monomers with their Binding Affinity at the Novel Polyester Hydrolase Target using Conceptual DFT and Machine Learning

The binding of short plastic polymer intermediates or monomers on microbial enzymes is a very important process in polymer degradation and the release of energy for the survival of the microbes. In this study, the molecular docking of six plastic monomers at the novel polyester hydrolase Leipzig 7 (PHL7) active site was performed to determine their selectivity at the enzyme target. The conceptual density functional theory descriptors for the plastic monomers were correlated with their binding affinities at the enzyme target. The most important descriptors for binding of the monomers on the enzyme were predicted from artificial neural networks (ANN) analysis. The results showed that polyethylene terephthalate, polycarbonate, and polyamide, with binding affinities – 5.3 kcal/mol, – 5.3 kcal/mol, and – 4.7 kcal/mol, respectively, were the most stable compounds at the PHL7 target. The binding affinity gave a very good correlation with the binding energy of the monomers while correlating poorly with the total energy, HOMO energy, LUMO energy, energy gap, and electronic chemical potential.ANN analysis predicted that the monomers' binding energy and molecular mass were the most important properties for its binding at the PHL7 target and, by extension, its degradation.