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All Journal Journal of Tropical Life Science : International Journal of Theoretical, Experimental, and Applied Life Sciences Mutiara Medika: Jurnal Kedokteran dan Kesehatan Emerging Science Journal ANNALES BOGORIENSES Annales Bogorienses
BUDI SAKSONO
Research Center for Biotechnology, Lembaga Ilmu Pengetahuan Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Environmental Science Health Professions Immunology & microbiology Medicine & Pharmacology

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In-Silico Optimization of α-agarase for Sustainable Bioprospecting of Agaro-oligosaccharides: In-Silico Optimization of α-agarase for Sustainable Bioprospecting of Agaro-oligosaccharides Hanbal, Mayland Muhammad; Wijayanti, Nastiti; Tsabitah, Khansa; Zulfa, Amania; Tasania, Nadia Nisa; Saksono, Budi
Journal of Tropical Life Science Vol. 15 No. 2 (2025): In Press
Publisher : Journal of Tropical Life Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/gg6mkk56

Abstract

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.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Karim Ahyar Ahmad Arfah, Rugaiyah Deni Zulfiana DEWI FITRIANI Ermawar, Riksfardini A. Hanbal, Mayland Muhammad Harningsih Karim Hasnah Natsir Irmawati Juliadmi, Dian Nastiti Wijayanti Pratitis, Visi Endah Puspitasari, Pinki Anggrahini Ramli, Siti R. Sita Heris Anita Tasania, Nadia Nisa Tsabitah, Khansa Wulandari, Puspita Suci Wulandari, Puspita Suci Zainuddin, Rahmaniah Zulfa, Amania