A computational approach to targeted amino acid mutagenesis for enhancing enzyme efficiency through improved enzyme-substrate dynamics and thermostability

Advancements in computational protein engineering have enabled the precise optimisation of enzyme stability and catalytic efficiency. This study employs site-directed amino acid-specific mutagenesis to enhance protein-ligand binding affinity while preserving structural integrity. Molecular docking analysis showed significant binding free energy (ΔG) improvements, with 1FCE_Thr226Leu_Cellulose (-7.2160 kcal/mol → -8.1532 kcal/mol, + 13.0%), 1FCE_Pro174Ala_AVICEL (-7.2160 kcal/mol → -8.8992 kcal/mol, + 23.3%), and 1AVA_Asp126Arg_Starch (-5.2035 kcal/mol → -7.5767 kcal/mol, + 45.6%). Ramachandran plot analysis confirmed minimal deviations (≤ 0.6%) in structural stability. RMSF analysis indicated increased flexibility at key residues, with peak shifts of 0.2–0.5 Å, supporting enhanced adaptability. Molecular Dynamics Simulations (MDS) verified stability, with wild-type 1FCE stabilising at 0.25 nm RMSD and mutants at 0.26 nm. Thermodynamic analysis showed Tm variations within ± 1.3 °C, ensuring mutation resilience (1FCE: 74.7 °C → 75.1 °C, 1AVA: 67.9 °C → 67.8 °C, 6M4K: 62.4 °C → 62.1 °C). pH-dependent aggregation analysis confirmed broader enzyme stability across pH 5.0–8.5, enhancing industrial applicability. Integrating MEME, SWOTein, SIAS Analysis, CABS-Flex 2.0, and WebGRO, this study offers a comprehensive approach to enzyme optimisation. The results demonstrate that computational mutagenesis significantly improves enzyme efficiency, making this a promising strategy for industrial biocatalysts in biotechnology, pharmaceuticals, and biofuels.