B-P.10: Probing multi-omic datasets to unravel multi-stability mechanisms in CAZymes from extreme biomass-rich environments.
Author
Keywords
Glycoscience, CAZymes, multi-omics, dbCAN3.
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Carbohydrate-active enzymes (CAZymes) are important biocatalysts for industrial processes where harsh conditions are present, such as high temperatures, extreme pH, high shear stress, or high solids loading, but their stability mechanisms remain poorly understood. This is especially true when multiple stressors are relevant at the same time. In this project, we mine metagenomes from extreme biomass-rich environments to identify robust CAZyme candidates with predicted favourable stability across a range of parameters. We use dbCAN3 for accurate CAZyme annotation, combined with domain and sub-family assignment, a structural feature analysis, and in silico stability parameter predictions to spotlight enzymes with a likely tolerance to multi-stress biocatalysis conditions. The use of these tools will focus on glycoside hydrolases (GHs) that depolymerize complex polysaccharides. Candidate proteins are further evaluated for the presence of carbohydrate-binding modules (CBMs), and other sequence or structural features associated with thermostability, pH range, aggregation propensity, and secretion signals, used in a robustness scoring system. Selected hits will then be cloned, expressed, and experimentally characterized for enzymatic activity and stability parameters. This integrative pipeline aims to generate a curated library of multi-stable CAZymes from metagenomic sources to improve the understandings of the molecular features of CAZyme robustness. The resulting candidates may serve to validate and improve the accuracy of the stability predictors, as well as representing robust new biocatalysts for industrial processes.
Co-authors: Lauren Sara McKee, Johan Larsbrink, Pakinee Thianheng
Contact Attendee
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