Reverse prediction of carbohydrate esterase polysaccharide targets.

Area of Science:

• Biochemistry
• Genomics
• Enzymology

Background:

• Carbohydrate esterases (CEs) modify polysaccharides, crucial for biomass processing.
• Predicting CE substrate specificity is difficult due to enzyme diversity and limited characterization.
• Traditional sequence alignment methods struggle with distant CE homologs.

Purpose of the Study:

• To develop a novel framework for predicting carbohydrate esterase (CE) substrate specificity.
• To leverage genomic context and polysaccharide utilization loci (PULs) for target inference.
• To guide enzyme discovery and engineer enzyme cocktails for biomass valorization.

Main Methods:

• Introduced a reverse prediction framework integrating motif-based functional groups.
• Utilized large-scale co-occurrence analysis across Bacteroidota genomes.
• Analyzed genomic context and polysaccharide utilization loci (PULs) to infer CE targets.

Main Results:

• Identified substrate preferences at the clade level for 20 CE families.
• Subdividing CE families into clades revealed specific substrate targets.
• Expanded functional coverage by up to 50% compared to characterized members alone.

Conclusions:

• The framework successfully predicts specific CE targets like arabinoxylan, β-mannan, and pectin.
• Demonstrated substantial functional subdivision within CE families.
• Reverse prediction is a powerful tool for enzyme discovery and biomass valorization.