Identification of highly specific bacterial L-asparaginase through genome-wide computational screening and experimental validation

Background Bacterial L-asparaginases are key therapeutics for acute lymphoblastic leukemia (ALL), but their clinical efficacy is limited by off-target L-glutaminase co-activity, which contributes to severe side effects. This study aimed to identify highly specific bacterial L-asparaginases with minimal L-glutaminase co-activity. Methods We screened ~ 85,000 prokaryotic genomes using a workflow integrating AlphaFold2 structure prediction, molecular docking, binding-energy estimation, and molecular dynamics simulations. Six top-ranked enzymes were selected for experimental validation. Results RhAII protein from Acidobacteriota sp. showed high specificity toward L-asparagine (L-Asn), with a Michaelis constant (K M ) of 12 ± 2 µM and turnover number (k cat ) of 34 ± 2 s⁻¹, and negligible L-glutaminase activity. Molecular dynamics (MD) simulations revealed a relationship between enzyme activity and the conformational dynamics of the flexible gating element (FGE), characterized by residue-level root mean square fluctuation (RMSF), and L-Asn positional fluctuations, evaluated by ligand root mean square deviation (RMSD) relative to the protein. High L-asparaginase activity was associated with optimal FGE dynamics and low L-Asn RMSD, consistent with stable substrate positioning within the active site. In contrast, enzymes with altered FGE dynamics relative to the reference EcAII enzyme from Escherichia coli , such as PoAII from Planctomycetota sp ., showed poor L-asparaginase performance (K M 5797 ± 1143 µM; k cat 1.2 ± 0.1 s⁻¹). Conclusions Genome- and structure-guided screening identified RhAII as a promising antileukemic candidate for further validation in biological models. A key limitation of the computational workflow was the lack of correlation between predicted substrate binding energies and catalytic efficiency, highlighting the importance of active-site dynamics in enzyme kinetics.