Industrial enzymes such as proteases, amylases, an‎d phytases serve as crucial biocatalysts in industrial processes, yet their stability an‎d efficiency under harsh industrial conditions are often limited. In this study, advanced bioinformatics approaches were employed to design targeted mutations in functional hotspots of these enzymes to enhance their thermal stability, solubility, an‎d catalytic activity. The research began with extracting target enzyme sequences an‎d 3D structures from RCSB an‎d UniProt databases, followed by removal of redundant sequences using the CD-HIT algorithm. Sequence similarity network (SSN) analysis an‎d similarity matrices were then used to identify functionally related groups. HotSpot Wizard 3.0 was utilized to precisely identify hotspots by examining B-factors an‎d calculating Gibbs free energy changes (ΔΔG), which revealed key amino acids critical for enzyme stability an‎d function. Three-dimensional modeling of mutant proteins was performed using AlphaFold2, with structural quality assessed through pLDDT an‎d PAE metrics. Molecular docking was conducted using AutoDock Vina, while molecular dynamics simulations employed GROMACS to eva‎luate structural stability. The study identified 9 functional hotspots in amylase an‎d 7 in phytase, predominantly located in catalytic an‎d substrate-binding regions. In amylase, key residues included Glu251, Asp373, an‎d Arg443, which played vital roles in maintaining secondary structure an‎d substrate interactions. Notably, Glu251 facilitated hydrogen bonding with substrates while Arg443 mediated electrostatic interactions. In phytase, critical hotspots were Lys48, Asp230, an‎d Phe323, with Lys48 positioned at the enzymeʹs active site an‎d Asp230 contributing to structural stability under acidic pH conditions. Molecular dynamics simulations demonstrated that the Glu251Lys mutation in amylase an‎d Lys48Pro in phytase reduced structural fluctuations (RMSD) by 15% an‎d 14%, respectively. These mutations also significantly improved enzyme-substrate binding affinity, increasing from -6.5 to -6.9 kcal/mol in amylase an‎d from -6.3 to -7.0 kcal/mol in phytase. These findings demonstrate that targeted engineering of these functional hotspots represents an effective strategy for optimizing industrial enzymes, potentially leading to significant improvements in their industrial applications. The bioinformatics-driven approach developed in this study provides a robust framework for enzyme enhancement an‎d could be extended to other industrially important enzymes.

ابوذر سورني , بدرالدين ابراهيم سيدطباطبايي

مهدي رحيم ملك , حسين رضوي زادگان جهرمي