<![CDATA[Enzymatic synthesis is a sustainable alternative to chemical methods, offering high substrate specificity, reduced byproducts, and environmentally friendly processes. Despite its advantages, agaro-oligosaccharides (AOS) production largely depends on chemical synthesis due to the limited exploration of α-agarase. Therefore, this study aims to identify, analyze, and redesign a suitable α-agarase candidate for AOS production using in-silico approaches. Biological databases from CAZy, UniProtKB, and PDB, along with journal references, were used to curate α-agarase candidates. Non-catalytic regions were removed to retain only the GH96 catalytic domain, with a His-tag added for easier purification. Subsequently, structural modeling using SWISS-MODEL was performed to facilitate blind docking with CB-Dock2. Modeling also facilitated physicochemical properties predictions incorporating OphPred, Protein-Sol, and SCooP for pH, solubility, and thermal stability. The results showed that AgaA33, obtained from Thalassomonas agarivorans JAMB-A33, was selected due to its high annotation score and optimal temperature. Structural modeling and blind docking confirmed that the functional domains were preserved after redesign. In-silico physicochemical assessments revealed that the redesigned enzyme exhibited improved pH tolerance and thermal stability, despite a slight reduction in solubility. This study showed the use of computational tools for enzyme redesign and showed the potential of α-agarase as a green and sustainable biocatalyst for AOS production. By combining database-driven candidate selection with in-silico structural and functional analyses, these results set the foundation for further optimization of α-agarase to meet industrial needs. Future efforts must focus on improving solubility and refining activity predictions to fully realize the enzyme’s potential for eco-friendly bioprocesses.]]>
