Computational analysis of enzyme specificity by molecular docking and quantum mechanical modeling of catalytic active site

Enzymes exhibit high substrate specificity which provides important information for designing new enzymes with enhanced specificity, selectivity, and stability. Enzyme specificity can be analyzed using various computational methods based on current research knowledge. In systems where the target molecule or substrate is known, molecular docking analysis serves as a useful tool; if the target is unknown, catalytical active site models emerge as a promising approach to analyze enzyme specificity. This research focuses on two systems to explore the enzyme specificity: (1) Developing depolymerizing enzymes for polyhydroxyalkanoate bioplastics. Polyhydroxyalkanoates (PHAs) have emerged as suitable bioplastic alternatives, yet their degradation present challenges. This research aims to enhance PHA depolymerase activity through molecular docking to analyze enzyme specificity. Based on molecular docking and near attack conformation (NAC) analysis, 3- hydroxyoctanoic acid p-nitrophenol was identified as the most favorable ligand for each protein that was studied in this research. (2) Designing catalytically active site models for the nerve agent hydrolysis. Nerve agents are organophosphorus compounds that inhibit acetylcholinesterase (AChE), leading to nervous system failure and death. Therefore, finding new enzymes that can bind nerve agents more strongly than AChE and hydrolyze them is in great importance. Active site models were used in this study, revealed nucleophile that attack phosphorous group axially as the lowest-energy configuration, with Ser–Lys and Asp–His as the optimal catalytic groups, and Soman as the most easily degradable nerve agent. Both studies revealed significant developments in predicting enzyme-substrate interactions, while molecular docking simulations identified key binding residues in PHO depolymerase, active site models that used in nerve agent hydrolysis captured critical features for enzyme specificity. These outcomes provided a strong foundation for the upcoming computational tool that will be facilitate enzyme specificity analysis.