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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

Journal of Tropical Life Science : International Journal of Theoretical, Experimental, and Applied Life Sciences · Vol. 15 No. 2 (2025) · hlm. 351–366

DOI
10.11594/gg6mkk56
ISSN
2527-4376, 2087-5517
Terbit
2025-08-26
Penerbit
Journal of Tropical Life Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

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.

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Sumber metadata: OAI-PMH (metadata jurnal) http://jtrolis.ub.ac.id/index.php/jtrolis/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">In-Silico Optimization of α-agarase for Sustainable Bioprospecting of Agaro-oligosaccharides: In-Silico Optimization of α-agarase for Sustainable Bioprospecting of Agaro-oligosaccharides</dc:title>
	<dc:creator xml:lang="en">Hanbal, Mayland Muhammad </dc:creator>
	<dc:creator xml:lang="en">Wijayanti, Nastiti</dc:creator>
	<dc:creator xml:lang="en">Tsabitah, Khansa</dc:creator>
	<dc:creator xml:lang="en">Zulfa, Amania</dc:creator>
	<dc:creator xml:lang="en">Tasania, Nadia Nisa</dc:creator>
	<dc:creator xml:lang="en">Saksono, Budi</dc:creator>
	<dc:subject xml:lang="en">α-agarase</dc:subject>
	<dc:subject xml:lang="en">AOS</dc:subject>
	<dc:subject xml:lang="en">in-silico</dc:subject>
	<dc:subject xml:lang="en">modelling</dc:subject>
	<dc:subject xml:lang="en">Thalassomonas agarivorans JAMB-A33</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:publisher xml:lang="en">Journal of Tropical Life Science</dc:publisher>
	<dc:date>2025-08-26</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jtrolis.ub.ac.id/index.php/jtrolis/article/view/3619</dc:identifier>
	<dc:identifier>10.11594/gg6mkk56</dc:identifier>
	<dc:source xml:lang="en">Journal of Tropical Life Science; Vol. 15 No. 2; 351 – 366</dc:source>
	<dc:source>2527-4376</dc:source>
	<dc:source>2087-5517</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jtrolis.ub.ac.id/index.php/jtrolis/article/view/3619/804</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Journal of Tropical Life Science</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>