Joji K, Santhiagu A, Salim Nisha
Bioprocess Laboratory, School of Biotechnology, National Institute of Technology, Calicut, 673601 India.
3 Biotech. 2019 Sep;9(9):323. doi: 10.1007/s13205-019-1853-y. Epub 2019 Aug 6.
The present study reports the optimized production and purification of an extremely active fibrinolytic enzyme from newly isolated marine bacterium sp. strain SKA27, with a specific activity of 125,107.85 U/mg and an apparent molecular weight of 28 kDa on SDS-PAGE. Wheat bran extract used for submerged production proved to be highly beneficial and enhanced fibrinolytic enzyme production when combined with yeast extract and CaCl. Optimization of culture media by response surface methodology (RSM) resulted in high root mean square error (RMSE), which led to the training of a back propagation multilayer artificial neural network (ANN) with 3-5-1 topology for better prediction quality. The prediction and optimization capabilities of regression and ANN were critically examined and ANN displayed higher proficiency with of 0.99 and RMSE of 2.0 compared to 0.98 and 48.9 RMSE of the regression model. An adept ANN linked genetic algorithm (GA) optimized the medium components to achieve 1.8-fold higher enzyme production (4175.41 U/mL). Further, a new and improved in vitro qualitative analysis displayed high specificity of purified enzyme to fibrin.
本研究报告了从新分离的海洋细菌sp.菌株SKA27中优化生产和纯化一种极具活性的纤溶酶，其在SDS-PAGE上的比活性为125,107.85 U/mg，表观分子量为28 kDa。事实证明，用于深层发酵生产的麦麸提取物非常有益，与酵母提取物和氯化钙结合使用时可提高纤溶酶的产量。通过响应面法（RSM）优化培养基导致均方根误差（RMSE）较高，这促使训练了一个具有3-5-1拓扑结构的反向传播多层人工神经网络（ANN），以获得更好的预测质量。对回归和人工神经网络的预测及优化能力进行了严格检验，结果表明，与回归模型的0.98 和48.9 RMSE相比，人工神经网络的熟练度更高，相关系数为0.99，RMSE为2.0。一个熟练的人工神经网络连接遗传算法（GA）优化了培养基成分，使酶产量提高了1.8倍（4175.41 U/mL）。此外，一种新的改进的体外定性分析表明，纯化后的酶对纤维蛋白具有高度特异性。
