RESUMEN
Understanding the mechanisms influencing poly(ethylene terephthalate) (PET) biodegradation is crucial for developing innovative strategies to accelerate the breakdown of this persistent plastic. In this study, we employed all-atom molecular dynamics simulation to investigate the adsorption process of the LCC-ICCG cutinase enzyme onto the PET surface. Our results revealed that hydrophobic, π-π, and H bond interactions, specifically involving aliphatic, aromatic, and polar uncharged amino acids, were the primary driving forces for the adsorption of the cutinase enzyme onto PET. Additionally, we observed a negligible change in the enzyme's tertiary structure during the interaction with PET (RMSD = 1.35 Å), while its secondary structures remained remarkably stable. Quantitative analysis further demonstrated that there is about a 24% decrease in the number of enzyme-water hydrogen bonds upon adsorption onto the PET surface. The significance of this study lies in unraveling the molecular intricacies of the adsorption process, providing valuable insights into the initial steps of enzymatic PET degradation.
Asunto(s)
Hidrolasas de Éster Carboxílico , Estabilidad de Enzimas , Simulación de Dinámica Molecular , Tereftalatos Polietilenos , Tereftalatos Polietilenos/química , Tereftalatos Polietilenos/metabolismo , Adsorción , Hidrolasas de Éster Carboxílico/química , Hidrolasas de Éster Carboxílico/metabolismo , Enlace de Hidrógeno , Interacciones Hidrofóbicas e HidrofílicasRESUMEN
With the aim of producing realistic coarse-grained models of homopolymers, we introduce a tabulated backbone-oriented anisotropic potential. The parameters of the model are optimized using statistical trajectory matching. The impact of grain anisotropy is evaluated at different coarse-graining levels using cis-polybutadiene as a test case. We show that, at the same time, tuning the aspect ratio of the grains can lead to a better density and structure and may reduce the unphysical bond crossings by up to 90%, without increasing the computation time too much and thereby jeopardizing the main advantage of coarse-grained models.