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用于利用毕赤酵母甲醇诱导培养物进行膜结合儿茶酚-O-甲基转移酶生物合成的人工神经网络。

An artificial neural network for membrane-bound catechol-O-methyltransferase biosynthesis with Pichia pastoris methanol-induced cultures.

作者信息

Pedro Augusto Q, Martins Luís M, Dias João M L, Bonifácio Maria J, Queiroz João A, Passarinha Luís A

机构信息

CICS-UBI, Centro de Investigação em Ciências da Saúde, Universidade da Beira Interior, Avenida Infante D. Henrique, 6201-001, Covilhã, Portugal.

Department of Biochemistry, Cambridge System Biology Centre, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge, CB2 1GA, UK.

出版信息

Microb Cell Fact. 2015 Aug 7;14:113. doi: 10.1186/s12934-015-0304-7.

Abstract

BACKGROUND

Membrane proteins are important drug targets in many human diseases and gathering structural information regarding these proteins encourages the pharmaceutical industry to develop new molecules using structure-based drug design studies. Specifically, membrane-bound catechol-O-methyltransferase (MBCOMT) is an integral membrane protein that catalyzes the methylation of catechol substrates and has been linked to several diseases such as Parkinson's disease and Schizophrenia. Thereby, improvements in the clinical outcome of the therapy to these diseases may come from structure-based drug design where reaching MBCOMT samples in milligram quantities are crucial for acquiring structural information regarding this target protein. Therefore, the main aim of this work was to optimize the temperature, dimethylsulfoxide (DMSO) concentration and the methanol flow-rate for the biosynthesis of recombinant MBCOMT by Pichia pastoris bioreactor methanol-induced cultures using artificial neural networks (ANN).

RESULTS

The optimization trials intended to evaluate MBCOMT expression by P. pastoris bioreactor cultures led to the development of a first standard strategy for MBCOMT bioreactor biosynthesis with a batch growth on glycerol until the dissolved oxygen spike, 3 h of glycerol feeding and 12 h of methanol induction. The ANN modeling of the aforementioned fermentation parameters predicted a maximum MBCOMT specific activity of 384.8 nmol/h/mg of protein at 30°C, 2.9 mL/L/H methanol constant flow-rate and with the addition of 6% (v/v) DMSO with almost 90% of healthy cells at the end of the induction phase. These results allowed an improvement of MBCOMT specific activity of 6.4-fold in comparison to that from the small-scale biosynthesis in baffled shake-flasks.

CONCLUSIONS

The ANN model was able to describe the effects of temperature, DMSO concentration and methanol flow-rate on MBCOMT specific activity, as shown by the good fitness between predicted and observed values. This experimental procedure highlights the potential role of chemical chaperones such as DMSO in improving yields of recombinant membrane proteins with a different topology than G-coupled receptors. Finally, the proposed ANN shows that the manipulation of classic fermentation parameters coupled with the addition of specific molecules can open and reinforce new perspectives in the optimization of P. pastoris bioprocesses for membrane proteins biosynthesis.

摘要

背景

膜蛋白是许多人类疾病中的重要药物靶点,收集有关这些蛋白的结构信息有助于制药行业利用基于结构的药物设计研究开发新分子。具体而言,膜结合儿茶酚-O-甲基转移酶(MBCOMT)是一种整合膜蛋白,可催化儿茶酚底物的甲基化,并且与帕金森病和精神分裂症等多种疾病有关。因此,改善这些疾病的治疗临床结果可能来自基于结构的药物设计,其中获得毫克量的MBCOMT样品对于获取有关该靶蛋白的结构信息至关重要。因此,这项工作的主要目的是使用人工神经网络(ANN)优化毕赤酵母生物反应器甲醇诱导培养物中重组MBCOMT生物合成的温度、二甲基亚砜(DMSO)浓度和甲醇流速。

结果

旨在评估毕赤酵母生物反应器培养物中MBCOMT表达的优化试验导致开发了一种用于MBCOMT生物反应器生物合成的首个标准策略,即在甘油上分批生长直至溶解氧峰值,3小时甘油进料和12小时甲醇诱导。上述发酵参数的ANN建模预测,在30°C、2.9 mL/L/H甲醇恒定流速以及添加6%(v/v)DMSO的条件下,诱导期结束时MBCOMT的最大比活性为384.8 nmol/h/mg蛋白,几乎90%的细胞健康。与在带挡板摇瓶中的小规模生物合成相比,这些结果使MBCOMT的比活性提高了6.4倍。

结论

ANN模型能够描述温度、DMSO浓度和甲醇流速对MBCOMT比活性的影响,预测值与观测值之间的良好拟合表明了这一点。该实验程序突出了化学伴侣如DMSO在提高与G偶联受体拓扑结构不同的重组膜蛋白产量方面的潜在作用。最后,所提出的ANN表明,操纵经典发酵参数并添加特定分子可以为优化毕赤酵母膜蛋白生物合成生物过程开辟并强化新的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e24d/4527236/5f3959c28d85/12934_2015_304_Fig1_HTML.jpg

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