In Silico Screening and Structural Analysis of Thermostable Enzymes for Industrial Biotechnology

Thermostable enzymes are increasingly important for industrial biocatalysis because their enhanced structural stability enables catalytic activity under elevated temperatures and other demanding process conditions. In this study, an integrated computational framework was developed to identify and characterize potential thermostable lipases from large-scale metagenomic and enzyme sequence resources. Candidate enzymes were initially screened using sequence-composition characteristics and thermophilic structural signatures, followed by structural homology analysis using Foldseek. Potentially promising candidates were further evaluated through molecular dynamics simulations to assess conformational stability at elevated temperatures. Additional structural analyses focused on hydrophobic-core packing, disulfide bridge formation, hydrogen-bonding interactions, and local flexibility as potential determinants of thermostability. Computational stability assessment using PROSS and Rosetta-based energetic analysis was incorporated to evaluate structural integrity and favorable residue interactions while preserving catalytic-site architecture. Machine-learning-based prediction was additionally considered for identifying potentially destabilizing substitutions and refining the candidate pool. The screening pipeline identified three promising lipase candidates, designated LIP-01, LIP-04, and LIP-09, with predicted melting temperatures exceeding the predefined 85°C threshold and conserved catalytic residues. Molecular dynamics analysis over 200 ns indicated stable structural behavior at 50°C, while structural examination suggested that enhanced hydrophobic packing, disulfide connectivity, and persistent hydrogen-bonding interactions contributed to the predicted thermal robustness of the candidates. The R209 region, together with interactions involving H198 and N247, emerged as a potentially important structural feature in Thermomyces lanuginosus lipase variants. Collectively, these findings demonstrate the potential of an integrated sequence-, structure-, simulation-, and machine-learning-based strategy for prioritizing thermostable lipases for subsequent experimental validation and industrial biocatalytic applications.