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最大限度地提高蛋白质的天然浓度和货架寿命:减少聚集的多目标优化。

Maximizing the native concentration and shelf life of protein: a multiobjective optimization to reduce aggregation.

机构信息

Department of Biotechnology, Integral University, Lucknow 226021, India.

出版信息

Appl Microbiol Biotechnol. 2011 Jan;89(1):99-108. doi: 10.1007/s00253-010-2835-5. Epub 2010 Aug 27.

Abstract

A multiobjective optimization was performed to maximize native protein concentration and shelf life of ASD, using artificial neural network (ANN) and genetic algorithm (GA). Optimum pH, storage temperature, concentration of protein, and protein stabilizers (Glycerol, NaCl) were determined satisfying the twin objective: maximum relative area of the dimer peak (native state) after 48 h of storage, and maximum shelf life. The relative area of the dimer peak, obtained from size exclusion chromatography performed as per the central composite design (CCD), and shelf life (obtained as turbidity change) served as training targets for the ANN. The ANN was used to establish mathematical relationship between the inputs and targets (from CCD). GA was then used to optimize the above determinants of aggregation, maximizing the twin objectives of the network. An almost fourfold increase in shelf life (~196 h) was observed at the GA-predicted optimum (protein concentration: 6.49 mg/ml, storage temperature: 20.8 °C, Glycerol: 10.02%, NaCl: 51.65 mM and pH: 8.2). Since no aggregation was observed at the optimum till 48 h, all the protein was found at the dimer position with maximum relative area (64.49). Predictions of the finally adapted network also reveal that storage temperature and solvent glycerol concentration plays key role in deciding the degree of ASD aggregation. This multiobjective optimization strategy was also successfully applied in minimizing the batch culture period and determining optimum combination of medium components required for most economical production of actinomycin D.

摘要

采用人工神经网络(ANN)和遗传算法(GA)对 ASD 的天然蛋白浓度和货架期进行了多目标优化。通过满足两个目标:储存 48 小时后二聚体峰(天然状态)的相对面积最大,以及货架期最长,确定了最佳 pH 值、储存温度、蛋白浓度和蛋白稳定剂(甘油、NaCl)。通过中心复合设计(CCD)进行的排阻色谱法获得的二聚体峰的相对面积和货架期(通过浊度变化获得)用作 ANN 的训练目标。ANN 用于建立输入和目标(来自 CCD)之间的数学关系。然后,GA 用于优化聚集的上述决定因素,使网络的两个目标最大化。在 GA 预测的最佳条件下(蛋白浓度:6.49mg/ml,储存温度:20.8°C,甘油:10.02%,NaCl:51.65mM 和 pH:8.2),货架期观察到近四倍的延长(~196h)。由于在最佳条件下直到 48 小时都没有观察到聚集,因此所有蛋白都处于二聚体位置,具有最大的相对面积(64.49)。最终适应的网络的预测也表明,储存温度和溶剂甘油浓度在决定 ASD 聚集程度方面起着关键作用。该多目标优化策略还成功应用于最小化分批培养周期,并确定了最经济生产放线菌素 D 的培养基成分的最佳组合。

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