Department of Pharmaceutical Biotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, ValiAsr Avenue, Niayesh Junction, PO Box 14155-6153, Tehran, Iran.
Sci Rep. 2022 Mar 31;12(1):5463. doi: 10.1038/s41598-022-09500-6.
The solubility of proteins is usually a necessity for their functioning. Recently an emergence of machine learning approaches as trained alternatives to statistical models has been evidenced for empirical modeling and optimization. Here, soluble production of anti-EpCAM extracellular domain (EpEx) single chain variable fragment (scFv) antibody was modeled and optimized as a function of four literature based numerical factors (post-induction temperature, post-induction time, cell density of induction time, and inducer concentration) and one categorical variable using artificial neural network (ANN) and response surface methodology (RSM). Models were established by the CCD experimental data derived from 232 separate experiments. The concentration of soluble scFv reached 112.4 mg/L at the optimum condition and strain (induction at cell density 0.6 with 0.4 mM IPTG for 24 h at 23 °C in Origami). The predicted value obtained by ANN for the response (106.1 mg/L) was closer to the experimental result than that obtained by RSM (97.9 mg/L), which again confirmed a higher accuracy of ANN model. To the author's knowledge this is the first report on comparison of ANN and RSM in statistical optimization of fermentation conditions of E.coli for the soluble production of recombinant scFv.
蛋白质的溶解性通常是其发挥功能的必要条件。最近，机器学习方法作为统计模型的替代方法，已经在经验建模和优化方面得到了证据。在这里，作为四个基于文献的数值因素（诱导后温度、诱导后时间、诱导时间的细胞密度和诱导剂浓度）和一个类别变量的函数，对抗-EpCAM 细胞外结构域（EpEx）单链可变片段（scFv）抗体的可溶性生产进行了建模和优化，使用人工神经网络（ANN）和响应面法（RSM）。模型是通过源于 232 个单独实验的 CCD 实验数据建立的。在最佳条件和菌株（在 23°C 下以 0.4mM IPTG 诱导细胞密度为 0.6，诱导 24 小时）下，可溶性 scFv 的浓度达到 112.4mg/L。ANN 对响应值（106.1mg/L）的预测值比 RSM （97.9mg/L）更接近实验结果，这再次证实了 ANN 模型的更高准确性。据作者所知，这是首次在大肠杆菌发酵条件的统计优化中比较 ANN 和 RSM 的报告，用于可溶性生产重组 scFv。
