Substrate docking and molecular dynamic simulation for prediction of fungal enzymes from Trichoderma species-assisted extraction of nanocellulose from oil palm leaves
Özet
Fungi of the Trichoderma species are valued industrial enzymes in support of the 'zero-waste' technology to convert agro-industrial biomass into valuable products, i.e. nanocellulose (NC). In this study, an in silico approach using substrate docking and molecular dynamic (MD) simulation was used to predict the order of which the multilayers of cellulosic polymers, i.e. lignin, hemicellulose and cellulose in oil palm leaves (OPL) are degraded by fungal enzymes, endocellulase and exocellulase. The study aimed to establish the catalytic tendencies of the enzymes to optimally degrade the cellulosic components of OPL for high yield production of NC. Energy minimized endocellulase and exocellulase models revealed satisfactory scores of PROCHECK (90.0% and 91.2%), Verify3D (97.23% and 98.85%) and ERRAT (95.24% and 91.00%) assessments. Active site prediction by blind docking, COACH meta-server and multiple sequence alignment indicated the catalytic triads for endocellulase and exocellulase were Ser116-His205-Glu249 and Ser382-Arg124-Asp385, respectively. Binding energy of endocellulase docked with hemicellulose (-6.0 kcal mol(-1)) was the most favourable followed by lignin (-5.6 kcal mol(-1)) and cellulose (-4.4 kcal mol(-1)). Exocellulase, contrarily, bonded favorably with lignin (-8.7 kcal mol(-1)), closely followed by cellulose (-8.5 kcal mol(-1)) and hemicellulose (-8.4 kcal mol(-1)). MDs simulations showed that interactions of complexes, endocellulase-hemicellulose and the exocellulase-cellulose being the most stable. Thus, the findings of the study successfully identified the specific actions of sugar-acting enzymes for NC production. Communicated by Ramaswamy H. Sarma
