Structural and Molecular Dynamics Analysis of Novel Bacterial Esterases: Insights into Substrate Binding and Thermostability for Rational Enzyme Desig

Bacterial esterases are pivotal biocatalysts with diverse industrial applications, yet their structural and dynamic mechanisms remain underexplored. This study investigates three novel esterases extracted and sequenced from Bacillus pumilus, Bacillus licheniformis, and Geobacillus kaustophilus, which were computationally modeled and functionally characterized for the first time. High-accuracy structural predictions were achieved using SWISS-MODEL (for B. pumilus and G. kaustophilus) and AlphaFold v2 (for B. licheniformis), with sequence identities exceeding 80–95% against their templates. Structural validation via MolProbity, PROCHECK, and QMEANDisCo confirmed stereochemical robustness (Ramachandran favored regions >90%, clash scores <2.0). Active site analysis using CASTp revealed conserved catalytic triads (Ser-His-Glu/Asp) and substrate-binding residues. Molecular docking with 4-nitrophenyl butyrate identified strong binding affinities (−4.93 to −5.21 kcal/mol), with B. pumilus exhibiting the lowest RMSD (0.78 Å). Molecular dynamics simulations (100 ns) highlighted dynamic stability, with progressive ligand burial (SASA reduction: 11–12%) and rigid torsional profiles. Notably, B. pumilus and G. kaustophilus displayed enhanced binding over time due to optimized van der Waals (−27.68 and −27.46 kcal/mol) and lipophilic interactions, while B. licheniformis showed weakened affinity, attributed to elevated ligand strain. Secondary structures remained stable (α-helices: ~70–75%), underscoring preserved enzymatic folds. This study advances the mechanistic understanding of esterase-ligand interactions, emphasizing the role of dynamic simulations in capturing time-dependent binding shifts. The findings position B. pumilus as a prime candidate for thermostability engineering and industrial biocatalysis, while B. licheniformis necessitates structural refinement. These insights bridge computational modeling with biotechnological applications, offering a framework for rational enzyme design.