Mechanistic Insights and Computer‐Informed Design of α‐Galactosidase for Galactooligosaccharide Synthesis

Abstract Microbial α‐galactosidases (AGals) are widely used in agriculture and food industries for degrading raffinose family oligosaccharides and synthesizing galactooligosaccharides (GOSs). While rational engineering of AGals is ongoing, limited understanding of substrate specificity and the determinants of hydrolysis and transglycosylation hinders progress. Here, we apply quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the catalytic mechanism and substrate specificity of Saccharomyces cerevisiae glycoside hydrolase family 27 (GH27) AGal. The enzyme catalyzes hydrolysis via a Koshland double‐displacement mechanism and cleaves linear galactomannans in an exo‐mode. Free‐energy calculations indicate glycosylation is the rate‐determining step with a barrier (Δ G ‡ ) of 17.8 kcal·mol −1 , consistent with experimental data. A key 4‐OH···nucleophile interaction stabilizes the transition state, particularly for deglycosylation. Machine learning identified Trp188 and Phe235 at positive subsites as mutational hotspots. Six AGal variants were evaluated for in silico transglycosylation activity. Aromatic substitutions at Phe235 (F235Y and F235W) favored nucleophilic attack (NA) with sucrose, while W188A, W188R, and F235S showed low reaction barriers for lactose. The W188A variant showed improvement with a 10 kcal·mol −1 decrease in Δ G ‡ , a pronounced 0.3 Å shortening of NA distance, and an increased solvent exposure of ∼500–600 Å 2 . These results highlight the potential of computer‐aided subsite engineering to enhance AGal performance in GOS production.

Authors

Institutions

Publication Details

Journal
ChemCatChem
Published
2025-10-03
DOI
https://doi.org/10.1002/cctc.202501207
Citations
6
Primary Topic
Enzyme Production and Characterization
Type
article
Field-Weighted Citation Impact
7.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mechanistic Insights and Computer‐Informed Design of α‐Galactosidase for Galactooligosaccharide Synthesis

Tanchanok Wanjai, Ran Friedman, Jitrayut Jitonnom, Wijitra Jitonnom
6 citations
ChemCatChem
Enzyme Production and Characterization
7.40
article

Mechanistic Insights and Computer‐Informed Design of α‐Galactosidase for Galactooligosaccharide Synthesis

Tanchanok Wanjai, Ran Friedman, Jitrayut Jitonnom, Wijitra Jitonnom
article en
6 citations

Abstract

Abstract Microbial α‐galactosidases (AGals) are widely used in agriculture and food industries for degrading raffinose family oligosaccharides and synthesizing galactooligosaccharides (GOSs). While rational engineering of AGals is ongoing, limited understanding of substrate specificity and the determinants of hydrolysis and transglycosylation hinders progress. Here, we apply quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the catalytic mechanism and substrate specificity of Saccharomyces cerevisiae glycoside hydrolase family 27 (GH27) AGal. The enzyme catalyzes hydrolysis via a Koshland double‐displacement mechanism and cleaves linear galactomannans in an exo‐mode. Free‐energy calculations indicate glycosylation is the rate‐determining step with a barrier (Δ G ‡ ) of 17.8 kcal·mol −1 , consistent with experimental data. A key 4‐OH···nucleophile interaction stabilizes the transition state, particularly for deglycosylation. Machine learning identified Trp188 and Phe235 at positive subsites as mutational hotspots. Six AGal variants were evaluated for in silico transglycosylation activity. Aromatic substitutions at Phe235 (F235Y and F235W) favored nucleophilic attack (NA) with sucrose, while W188A, W188R, and F235S showed low reaction barriers for lactose. The W188A variant showed improvement with a 10 kcal·mol −1 decrease in Δ G ‡ , a pronounced 0.3 Å shortening of NA distance, and an increased solvent exposure of ∼500–600 Å 2 . These results highlight the potential of computer‐aided subsite engineering to enhance AGal performance in GOS production.

ChemCatChemVol. 17(22)
Linnaeus University (SE), University of Phayao (TH)
Openalex Percentile: Top 3%
Enzyme Production and Characterization
7.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.