Exploring the Catalytic Significant Residues of Serine Protease Using Substrate-Enriched Residues and a Peptidase Inhibitor
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Serine proteases are the most versatile proteolytic enzymes with tremendous applications in various industrial processes. This study was designed to investigate the biochemical properties, critical residues, and the catalytic potential of alkaline serine protease using in-silico approaches. The primary sequence was analyzed using ProtParam, SignalP, and Phyre2 tools to investigate biochemical properties, signal peptide, and secondary structure, respectively. The three-dimensional structure of the enzyme was modeled using the MODELLER program present in Discovery Studio followed by Molecular Dynamics simulation using GROMACS 5.0.7 package with CHARMM36m force field. The proteolytic potential was measured by performing docking with casein- and keratin-enriched residues, while the effect of the inhibitor was studied using phenylmethylsulfonyl fluoride, (PMSF) applying GOLDv5.2.2. Molecular weight, instability index, aliphatic index, and isoelectric point for serine protease were 39.53 kDa, 27.79, 82.20 and 8.91, respectively. The best model was selected based on the lowest MOLPDF score (1382.82) and DOPE score (-29984.07). The analysis using ProSA-web revealed a Z-score of -9.7, whereas 88.86% of the residues occupied the most favored region in the Ramachandran plot. Ser327, Asp138, Asn261, and Thr326 were found as critical residues involved in ligand binding and execution of biocatalysis. Our findings suggest that bioengineering of these critical residues may enhance the catalytic potential of this enzyme.
Abstract
Serine proteases are the most versatile proteolytic enzymes with tremendous applications in various industrial processes. This study was designed to investigate the biochemical properties, critical residues, and the catalytic potential of alkaline serine protease using in-silico approaches. The primary sequence was analyzed using ProtParam, SignalP, and Phyre2 tools to investigate biochemical properties, signal peptide, and secondary structure, respectively. The three-dimensional structure of the enzyme was modeled using the MODELLER program present in Discovery Studio followed by Molecular Dynamics simulation using GROMACS 5.0.7 package with CHARMM36m force field. The proteolytic potential was measured by performing docking with casein- and keratin-enriched residues, while the effect of the inhibitor was studied using phenylmethylsulfonyl fluoride, (PMSF) applying GOLDv5.2.2. Molecular weight, instability index, aliphatic index, and isoelectric point for serine protease were 39.53 kDa, 27.79, 82.20 and 8.91, respectively. The best model was selected based on the lowest MOLPDF score (1382.82) and DOPE score (-29984.07). The analysis using ProSA-web revealed a Z-score of -9.7, whereas 88.86% of the residues occupied the most favored region in the Ramachandran plot. Ser327, Asp138, Asn261, and Thr326 were found as critical residues involved in ligand binding and execution of biocatalysis. Our findings suggest that bioengineering of these critical residues may enhance the catalytic potential of this enzyme.
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