Machine learning-guided discovery of thermophilic carbonic anhydrases from environmental metagenomes.

ABSTRACT: Thermophilic carbonic anhydrases (CAs) are promising biocatalysts for carbon capture utilization and storage (CCUS) due to their stability and efficiency at elevated temperatures. This study presents a machine learning (ML)-guided approach to discover thermostable γ-class CA (γ-CA) from metagenomic datasets derived from Fang Hot Spring, Northern Thailand. To develop classification models, two sets of protein descriptors-dipeptide composition (DPC) and physicochemical/biochemical properties (AAindex)-were used to train classification models. Fourteen ML algorithms were systematically evaluated for each feature set. AdaBoost achieved the best performance for the DPC-based model, while LightGBM performed best with AAindex-based features. External validation with known CA sequences confirmed t

SUBMITTER: Pairoh S 

PROVIDER: S-EPMC12635333 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature