Metallothionein (MT) is a multifunctional metal-binding protein with broad applications in medicine, healthcare, and food industries, but its large-scale use is limited by inefficient industrial synthesis. To address this and obtain optimal fermentation parameters for large-scale MT production, this study used the recombinant marine-derived MT-producing Pichia pastoris strain SMD1168-MT. We first optimized the strain’s growth and induced fermentation conditions, then constructed a Back Propagation (BP) neural network model for in-depth parameter optimization and accurate MT expression prediction. Results showed the optimal growth conditions for SMD1168-MT were: 30 °C, initial pH 8.0, shaking speed 220 r/min, and 4% inoculum size. The BP model exhibited high accuracy (training set: R2 = 0.8430, MAE = 0.0129, RMSE = 0.0175; validation set: R2 = 0.8337, MAE = 0.0144, RMSE = 0.0174). Combined with Particle Swarm Optimization (PSO), the optimal fermentation conditions were: 7.7% methanol, initial OD600 8.2, 240 r/min, 50 h induction, and 125 μmol/L Zn2+. Validation confirmed MT expression reached 0.2141 mg/mL (2.93-fold). This study demonstrates that the BP neural network effectively optimizes recombinant P. pastoris-based marine-derived MT fermentation, improving yield and providing a basis for industrial scale-up.
Yan et al. (Sat,) studied this question.