In silico mutagenesis of lignocellulolytic enzymes, depending on the propensity, causes enhancement in the spontaneity of the enzyme kinetics

• Computational screening of Native lignocellulolytic proteins. • Computational site-directed mutagenesis depending upon the propensity of amino acid . • Comparative analysis of Wild-type and mutated lignocellulolytic proteins computationally. • Validation of computational study by MD simulation. Agricultural waste is the primary source of lignocellulosic biomass (LCB), which can be degraded via various lignocellulolytic enzymes. Stenotrophomonas maltophilia, Bacillus licheniformis , and Pseudomonas aeruginosa are potential enzyme producers that can produce all the necessary enzymes for LCB breakdown. However, the amount of enzyme produced is insufficient to meet industrial needs. Hence, the improvement of enzyme productivity is a major concern for industry. Wet lab experiments have improved enzyme productivity, but the results fluctuate greatly. Thus, in the shadow of this problem, dry laboratory-based in silico experiments may provide some light with high accuracy. Different mechanisms of mutation are available through in silico techniques to increase productivity. However, the concept of propensity-dependent mutagenesis with decreasing ΔG and full fitness of the protein‒ligand interaction is still a phenomenal concept. To this end, this in silico work proves this hypothesis and validates results by using different computational tools, such as SwissDock for docking analysis, BIOVIA Discovery Studio for protein preparation, CB-Dock2 for calculating protein‒ligand interactions, UCSF Chimera for visualizing molecular structures, Agadir for propensity, Ramachandran plot for protein helicity, PyMOL for mutagenesis, the MEME suite for finding protein motifs, SIAS statistical analysis for evaluating sequence similarity, fastDRH for forecasting protein‒ligand complexes, SCooP analysis for enzyme reaction kinetics, MEGA 11 for phylogenetic study, CABS-flex 2.0 for the prediction of protein flexibility, WEBGRO for molecular dynamics simulation, and heatmaps for validating numerical data in the wild-type and mutant proteins. This concept will soon play a significant role in industrial research.