2026-04-13T02:29:32Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.14342/54982025-09-13T23:58:17Zcom_2072_482405com_2072_183628col_2072_482414

00925njm 22002777a 4500 dc Vega, Aitor author Planas, Antoni author Biarnés, Xevi author 2025-08 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 kcat/KM 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. 1742-4658 http://hdl.handle.net/20.500.14342/5498 https://doi.org/10.1111/febs.70121 Binding affinity Computational protein engineering Electrostatic potential Glycoside hydrolases Tansglycosylation Biologia computacional Enzims Electroestàtica Glicòsids Electrostatic potential as a reactivity scoring function in computer-assisted enzyme engineering