biostudies-literatureVega ARamon Llull University/Obra Social la CaixaMinistry of Science and Innovation, Spain4211-4231https://www.ebi.ac.uk/biostudies/studies/S-EPMC12366256biostudies-literatureUnknown292(16)The high catalytic efficiency of enzymes is attained, in part, by their capacity to stabilize electrostatically the transition state of the chemical reaction. High-throughput protocols for measuring this electrostatic contribution in computer-assisted enzyme design are limited. We present here an easy-to-compute metric that captures the electrostatic complementarity of the enzyme to the charge distribution of the substrate at the transition state. We demonstrate such a complementarity for a representative dataset of glycoside hydrolases, a large family of enzymes responsible for the hydrolytic cleavage of glycosidic bonds in oligosaccharides, polysaccharides, and glycoconjugates. We have implemented this metric in BindScan, a computer-based mutational analysis protocol to assist protein engineering. We demonstrate the predictive power of BindScan with this metric for two mechanistically distinct glycoside hydrolases: Spodoptera frugiperda β-glucosidase (Sfβgly, operates via protein nucleophile catalysis) and Bifidobacterium bifidum lacto-N-biosidase (BbLnbB, operates via substrate-assisted catalysis). The metric correctly predicts sequence positions sensible to the modulation of k_cat/K_M upon mutation from an experimental benchmark of 51 mutants of Sfβgly with 77% classification efficiency and identifies variants of BbLnbB with improved transglycosylation yields (up to 32%). Based on electrostatic potential and ligand affinity calculations, as implemented in BindScan, we propose a rational strategy to design glycoside hydrolase variants with improved transglycosylation efficiency for the synthesis of added-value glycoconjugates. The new reactivity metric may contribute to expanding the range of computational protocols available to assist enzyme engineering campaigns aimed at optimizing mechanistically relevant properties.The FEBS journalElectrostatic potential as a reactivity scoring function in computer-assisted enzyme engineering.PMC12366256GLYCODESIGN (PID2019-104350RB-I00)BINDSCAN 2.0'2020GLYCOENGIN (PID2022-138252OB-I00)Vega APlanas ABiarnes XfalseElectrostatic potential as a reactivity scoring function in computer-assisted enzyme engineering.The high catalytic efficiency of enzymes is attained, in part, by their capacity to stabilize electrostatically the transition state of the chemical reaction. High-throughput protocols for measuring this electrostatic contribution in computer-assisted enzyme design are limited. We present here an easy-to-compute metric that captures the electrostatic complementarity of the enzyme to the charge distribution of the substrate at the transition state. We demonstrate such a complementarity for a representative dataset of glycoside hydrolases, a large family of enzymes responsible for the hydrolytic cleavage of glycosidic bonds in oligosaccharides, polysaccharides, and glycoconjugates. We have implemented this metric in BindScan, a computer-based mutational analysis protocol to assist protein engineering. We demonstrate the predictive power of BindScan with this metric for two mechanistically distinct glycoside hydrolases: Spodoptera frugiperda β-glucosidase (Sfβgly, operates via protein nucleophile catalysis) and Bifidobacterium bifidum lacto-N-biosidase (BbLnbB, operates via substrate-assisted catalysis). The metric correctly predicts sequence positions sensible to the modulation of k_cat/K_M upon mutation from an experimental benchmark of 51 mutants of Sfβgly with 77% classification efficiency and identifies variants of BbLnbB with improved transglycosylation yields (up to 32%). Based on electrostatic potential and ligand affinity calculations, as implemented in BindScan, we propose a rational strategy to design glycoside hydrolase variants with improved transglycosylation efficiency for the synthesis of added-value glycoconjugates. The new reactivity metric may contribute to expanding the range of computational protocols available to assist enzyme engineering campaigns aimed at optimizing mechanistically relevant properties.2025-01-01T00:00:00Z2025 Aug2026-05-29T14:29:47.613Z2026-04-08T05:05:23.825ZS-EPMC123662564032283810.1111/febs.70121