In Silico Characterization of Selected Carbohydrate Active Enzymes (CAZymes) from Malaysia's Unique Genome Assemblies Through Genomic Data Mining

Authors

  • Aisyah Mohamed Rehan Department of Chemical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Education Hub, KM 1, Jalan Panchor, 86400, Panchor, Johor, MALAYSIA. https://orcid.org/0000-0001-7841-1705
  • Alvin Owen Miharil@Jukiri Department of Chemical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Education Hub, KM 1, Jalan Panchor, 86400, Panchor, Johor, MALAYSIA.
  • Azzmer Azzar Abdul Hamid Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Kuantan Campus, Bandar Indera Mahkota, 25200 Kuantan, Pahang, MALAYSIA https://orcid.org/0000-0003-2404-6890
  • Kamarul Rahim Kamarudin Department of Technology and Natural Resources, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Education Hub, KM 1, Jalan Panchor, 84600 Panchor, Johor, MALAYSIA https://orcid.org/0000-0001-5695-838X

DOI:

https://doi.org/10.22452/

Keywords:

CAZymes, In-silico Protein Modeling, Genomic Data Mining

Abstract

In silico characterization of protein is usually performed to elucidate the structure and function of protein targets prior to experimental approach, which helps to narrow down target candidates, and reduce overall time, cost and effort. Carbohydrate-active enzymes (CAZymes) are enzymes involved in conversion of biomass to industrially valuable products. More research is needed to characterise CAZymes from Malaysia. This study aims to elucidate CAZymes from selected genome assemblies isolated in Malaysia by in silico genomic data mining. The National Center for Biotechnology Information database is utilized to investigate Malaysia's genome assemblies, discovering 1386 complete genomes. From 2019 to 2024, there are 34 bacterial genomes isolated by Malaysian researchers. CAZymes from six bacterial genomes were categorised. Three CAZymes from Thermobifida fusca were chosen for subsequent protein modelling due to their acceptable homology range to available protein structures. To characterise the structural and functional properties of these CAZymes, online protein modelling tools were used (dbCAN3 meta server, I-TASSER and ExPASy SWISS-MODEL), as well as to evaluate the quality of the protein models (Ramachandran plot assessment, Verify3D and QMEAN4 score). Then, the ligand-binding sites inside each CAZyme sequence were predicted using I-TASSER’s COACH algorithm. Three target CAZymes annotated as endo-1,4-beta-xylanase, amylo-alpha-1,6-glucosidase and endoglucanase respectively have been modelled, their protein model assessed for its quality, and their active sites and potential ligand predicted. Through in-silico characterization, the proteins’ structure and function have been elucidated. Future studies, employing refined tertiary models, protein-ligand docking, and molecular dynamics simulations, aim to identify novel inhibitors or activators. This will facilitate experimental validation and unlock diverse industrial biotechnology applications. Potential benefits include enhanced enzyme catalysis for biofuel production, development of novel biopharmaceuticals, and optimization of metabolic pathways for sustainable chemical synthesis. These findings could significantly impact industrial processes and contribute to bio-based economies.

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Published

30-06-2026