Statistical and neural network modeling of β-glucanase production by Streptomyces albogriseolus (PQ002238), and immobilization on chitosan-coated magnetic microparticles.

IF 5.1 3区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Nourhan H Elshami, Ghadir S El-Housseiny, Mahmoud A Yassien, Nadia A Hassouna
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引用次数: 0

Abstract

β-Glucanases are a series of glycoside hydrolases (GHs) that are of special interest for various medical and biotechnological applications. Numerous β-glucanases were produced by different types of microorganisms. Particularly, bacterial β-glucanases have the privilege of being stable, easily produced, and suitable for large-scale production. This study aimed for finding potent β-glucanase producing bacterial strains and optimizing its production. Soil samples from Egyptian governorates were screened for such strains, and 96 isolates were collected. The β-glucanase activity was qualitatively assessed and quantitatively measured using 3,5-dinitrosalicylic acid (DNS) method. The highest β-glucanase producing strain (0.74 U/ml) was identified as Streptomyces albogriseolus S13-1. The optimum incubation period and temperature, determined one-variable at a time, were estimated as 4 d and 45 ͦ C, respectively. Similarly, yeast β-glucan and beef extract were selected as the best carbon and nitrogen sources, with enzymatic activities of 0.74 and 1.12 U/ml, respectively. Other fermentation conditions were optimized through response surface methodology (RSM); D-optimal design (DOD) with a total of 28 runs. The maximum experimental β-glucanase activity (1.3 U/ml) was obtained with pH 6.5, inoculum volume of 0.5% v/v, agitation speed of 100 rpm, carbon concentration of 1% w/v, and nitrogen concentration of 0.11% w/v. This was 1.76-fold higher compared to unoptimized conditions. Using the same experimental matrix, an artificial neural network (ANN) was built to predict β-glucanase production by the isolated strain. Predicted β-glucanase levels by RSM and ANN were 1.79 and 1.32 U/ml, respectively. Both models slightly over-estimated production levels, but ANN showed higher predictivity and better performance metrics. The enzyme was partially purified through acetone precipitation, characterized, and immobilized on chitosan-coated iron oxide microparticles. The optimal pH and temperature for enzyme activity were 5 and 50 °C, respectively. The immobilized enzyme showed superior characters such as higher stability at temperatures 50, 60, and 70 °C compared to the free enzyme, and satisfactory reusability, losing only 30% of activity after 6 cycles of reuse.

白灰链霉菌(PQ002238)产β-葡聚糖酶的统计和神经网络建模,以及壳聚糖包覆磁微粒的固定化。
β-葡聚糖酶是一系列糖苷水解酶(GHs),在各种医学和生物技术应用中具有特殊的意义。许多β-葡聚糖酶由不同类型的微生物产生。细菌β-葡聚糖酶具有稳定性好、易于生产、适合大规模生产的特点。本研究旨在寻找高效生产β-葡聚糖酶的菌株并优化其生产。对埃及各省的土壤样本进行了这类菌株的筛选,并收集了96株分离株。采用3,5-二硝基水杨酸(DNS)法对β-葡聚糖酶活性进行定性评价和定量测定。产β-葡聚糖酶最高的菌株为白灰链霉菌S13-1 (0.74 U/ml)。每次确定一个变量的最佳孵育时间和温度分别为4天和45ºC。酵母β-葡聚糖和牛肉提取物为最佳碳源和氮源,酶活性分别为0.74和1.12 U/ml。通过响应面法(RSM)优化其他发酵条件;d -最优设计(DOD),共运行28次。当pH为6.5、接种量为0.5% v/v、搅拌速度为100 rpm、碳浓度为1% w/v、氮浓度为0.11% w/v时,β-葡聚糖酶活性最高(1.3 U/ml)。这比未优化的条件高1.76倍。利用相同的实验矩阵,构建人工神经网络(ANN)来预测分离菌株的β-葡聚糖酶产量。RSM和ANN预测β-葡聚糖酶水平分别为1.79和1.32 U/ml。两种模型都略微高估了生产水平,但人工神经网络显示出更高的预测性和更好的性能指标。通过丙酮沉淀法对酶进行了部分纯化,对酶进行了表征,并将酶固定在壳聚糖包被的氧化铁微粒上。酶活性的最佳pH和温度分别为5℃和50℃。与游离酶相比,固定化酶在50、60和70℃的温度下具有较高的稳定性,并且具有良好的可重复使用性,重复使用6次后活性仅损失30%。
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来源期刊
Bioresources and Bioprocessing
Bioresources and Bioprocessing BIOTECHNOLOGY & APPLIED MICROBIOLOGY-
CiteScore
7.20
自引率
8.70%
发文量
118
审稿时长
13 weeks
期刊介绍: Bioresources and Bioprocessing (BIOB) is a peer-reviewed open access journal published under the brand SpringerOpen. BIOB aims at providing an international academic platform for exchanging views on and promoting research to support bioresource development, processing and utilization in a sustainable manner. As an application-oriented research journal, BIOB covers not only the application and management of bioresource technology but also the design and development of bioprocesses that will lead to new and sustainable production processes. BIOB publishes original and review articles on most topics relating to bioresource and bioprocess engineering, including: -Biochemical and microbiological engineering -Biocatalysis and biotransformation -Biosynthesis and metabolic engineering -Bioprocess and biosystems engineering -Bioenergy and biorefinery -Cell culture and biomedical engineering -Food, agricultural and marine biotechnology -Bioseparation and biopurification engineering -Bioremediation and environmental biotechnology
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