Metagenomic discovery and computational screening of hyperthermophilic functional enzymes from Sungai Klah hot spring
This study presents a metagenomic exploration and computational screening pipeline for discovering novel hyperthermophilic enzymes from near-boiling hot spring environments in Sungai Klah, Malaysia (MGYS00001574). Utilizing high-throughput sequencing data from extreme thermal biomes (>90°C), we identified and curated a dataset of predicted thermostable proteins, including novel metalloproteases (M10 family), HAD-family hydrolases, and uncharacterized domain-of-unknown-function (DUF) family proteins. Potential candidates were computationally evaluated for thermostability scores and predicted optimal growth temperatures (80–89°C). Sequence sanitization and quality control pipelines were implemented to filter non-standard residues, remove sequence redundancies, and validate open reading frame (ORF) completeness. This dataset provides valuable bioinformatic resources and bio-catalytic candidates for extreme industrial biotechnology applications and AI-driven protein engineering.
This study presents a metagenomic exploration and computational screening pipeline for discovering novel hyperthermophilic enzymes from near-boiling hot spring environments in Sungai Klah, Malaysia (MGYS00001574). Utilizing high-throughput sequencing data from extreme thermal biomes (>90°C), we identified and curated a dataset of predicted thermostable proteins, including novel metalloproteases (M10 family), HAD-family hydrolases, and uncharacterized domain-of-unknown-function (DUF) family proteins. Potential candidates were computationally evaluated for thermostability scores and predicted optimal growth temperatures (80–89°C). Sequence sanitization and quality control pipelines were implemented to filter non-standard residues, remove sequence redundancies, and validate open reading frame (ORF) completeness. This dataset provides valuable bioinformatic resources and bio-catalytic candidates for extreme industrial biotechnology applications and AI-driven protein engineering.