Acid phosphatases (ACPase, EC 3.1.3.2) are hydrolytic enzymes widely distributed in both plant and animal tissues. Despite their ubiquitous presence, the production and specific activity of ACPase in these sources remain suboptimal. Consequently, the development of microbial cell factories for large-scale ACPase production has emerged as a significant research focus. In this study, we successfully expressed the phosphatase PAP2 family protein (acid phosphatase) from Acinetobacter nosocomialis 1905 in Bacillus subtilis 168. The specific activity of the crude enzyme solution was 59.60 U/mg. After purification, the enzyme activity increased to 86.62 U/mL, with a specific activity of 129.60 U/mg. Characterization of the enzyme revealed optimal activity at 45 °C and a pH of 6.0. The Km value was determined to be 0.25 mmol/L using p-nitrophenylphosphoric acid disodium salt as the substrate. Additionally, the enzyme activity was found to be enhanced by the presence of Ni²⁺. Dissolved oxygen and medium were subsequently optimized during fermentation on the basis of a commercially available 5 L bioreactor. The recombinant strain B. subtilis 168/pMA5-Acp achieved maximal volumetric enzyme activity of 136.9 U/mL after 12 h of fermentation under optimized conditions: an aeration rate of 1.142 VVM (4 lpm), an agitation speed of 350 rpm, and an optimal ratio of lactose to fish powder (7.5 g/L:12.5 g/L). These optimizations resulted in a 5.9-fold increase in volumetric enzyme activity, a 4.9-fold increase in enzyme synthesis per unit cell volume, and a 48.6% increase in biomass concentration. This study establishes a comprehensive technological framework for prokaryotic fermentation-based ACPase production, potentially addressing the bottleneck in industrial-scale applications.

Acid phosphatase (ACPase) is an essential industrial enzyme, but its production via recombinant bacterial fermentation is often limited by insufficient dissolved oxygen control. This study optimized the aerobic fermentation of the ACPase-producing recombinant bacterium Bacillus subtilis 168/pMA5-Acp by refining the bioreactor’s aerodynamic structure using computational fluid dynamics (CFD) simulations. This was combined with fermentation kinetics modeling to achieve precise process control. First, the gas distributor structure of the 5 L bioreactor was optimized using CFD simulation results. Optimal mass transfer conditions were identified through comprehensive analysis of K_La in different reactor regions (aeration ratio: 1.142 VVm, K_La = 264.2 h⁻¹). The simulation results showed that the optimized oxygen transfer efficiency increased 2.49 fold compared to the prototype. Second, the process control issue was addressed by developing a BP (backpropagation) neural network model to predict K_La under alternative media conditions. The prediction error was less than 5%, and the model was combined with the logistic equation to construct the bacterial growth kinetic model (R² > 0.99). The experiments demonstrated that using the optimized reactor with a molasses–urea medium (molasses 7.5 g/L; urea 15 g/L; K₂HPO₄ 1.2 g/L; MgSO₄·7H₂O 0.25 g/L) reduced production costs while maintaining enzyme activity (215.99 U/mL) and biomass (OD₆₀₀ = 101.67) by 90.03%. This study provides an efficient and cost-effective process solution for the industrial production of ACPase and a theoretical foundation for bioreactor design and scale-up.

Excessive application of chemical fertilisers has led to soil phosphorus immobilisation and aquatic eutrophication, making the development of highly efficient acid/neutral phosphatases crucial for sustainable phosphorus utilisation. In this study, we systematically investigated strain J2, which was isolated from phosphate-contaminated soil in Laoshan, Nanjing, China. 16S rRNA gene sequence analysis identified this strain as Acinetobacter nosocomialis J2, with 99.78% sequence similarity. Whole-genome sequencing generated a 3.83 Mb genome with a GC content of 38.59%, revealing multiple phospho-metabolism-related enzyme genes, including phospholipase C and α/β-hydrolases. A large language model–based protein representation learning strategy was employed to mine acid/neutral phosphatase genes from the genome, in which the model learned contextual and functional features from known phosphatase sequences and was used to identify semantically similar genes within the J2 genome. This approach predicted nine phosphatase candidate sequences, including AnACPase, a putative acid/neutral phosphatase. Biochemical characterisation showed that AnACPase exhibits optimal activity at pH 6.0 and 50 °C, with a K_m value of 0.2454 mmol/L for the p-NPP substrate, indicating high substrate affinity. Mn²⁺ and Ni²⁺ significantly enhanced enzyme activity, whereas Cu²⁺ and Zn²⁺ strongly inhibited it. Soil remediation experiments further validated the application potential of AnACPase, which solubilised 171.56 mg/kg of phosphate within seven days. Overall, this study highlights the advantages of deep learning-assisted genome mining for functional enzyme discovery and provides a novel technological pathway for the bioremediation of phosphorus-polluted soils.