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.
| Original language | English |
|---|---|
| Pages (from-to) | 65-74 |
| Number of pages | 12 |
| Journal | Microbiology and Biotechnology Letters |
| Volume | 49 |
| Issue number | 1 |
| Early online date | 28 Mar 2021 |
| DOIs | |
| Publication status | Published online - 28 Mar 2021 |
Funding
The authors acknowledge Higher Education Commission, Pakistan for the provision of funds for this study through TDF Grant No. 02-078 (awarded to corresponding author) and for Indigenous Ph.D. fellowship (awarded to first author).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Catalytic potential
- serine protease
- caseinolytic
- keratinolytic
- modelling
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