Deciphering the structural complexity of esterases in Amycolatopsis eburnea: A comprehensive exploration of solvent...

Carboxylesterases (CES) are pivotal enzymes in the hydrolysis of carboxylic esters, playing fundamental roles in both biological systems and biotechnological applications. This study investigates CES from the Amycolatopsis genus, characterized by its high GC content and structural complexity. Employing a machine learning-driven de novo modeling approach, we examined the primary sequences, physicochemical attributes, and structural characteristics of 109 CES proteins, including 23 from Amycolatopsis eburnea, which exhibit over 95 % sequence similarity to other species within the genus. Our analysis identified three distinct CES groups based on amino acid composition and molecular weight, with alanine, glycine, and valine as the most abundant residues. The isoelectric points varied from 4.9 to 10.27. Unsupervised agglomerative hierarchical clustering classified the CES into two major clusters, displaying >99.6 % structural similarity based on solvent accessibility. The average solvent-accessible surface area (SASA) was 9750 Å², with backbone regions exhibiting greater solvent exposure than side chains (7888 Å² vs. 3037 Å²). Key structural hot spots crucial for enzyme stability and folding were identified, offering potential targets for protein engineering. These findings provide valuable insights into the structural determinants of CES function, enabling rational design strategies to enhance enzyme performance and stability for biotechnological applications.