Molecular Biology Department, Genetic Engineering and Biotechnology Division, National Research Centre, El Behoth St, Dokki, Cairo, Egypt.
Folia Microbiol (Praha). 2022 Apr;67(2):253-264. doi: 10.1007/s12223-021-00929-2. Epub 2021 Nov 6.
Production of amylases by fungi under solid-state fermentation is considered the best methodology for commercial scaling that addresses the ever-escalating needs of the worldwide enzyme market. Here response surface methodology (RSM) was used for the optimization of process variables for α-amylase enzyme production from Trichoderma virens using watermelon rinds (WMR) under solid-state fermentation (SSF). The statistical model included four variables, each detected at two levels, followed by model development with partial purification and characterization of α-amylase. The partially purified α-amylase was characterized with regard to optimum pH, temperature, kinetic constant, and substrate specificity. The results indicated that both pH and moisture content had a significant effect (P < 0.05) on α-amylase production (880 U/g) under optimized process conditions at a 3-day incubation time, moisture content of 50%, 30 °C, and pH 6.98. Statistical optimization using RSM showed R values of 0.9934, demonstrating the validity of the model. Five α-amylases were separated by using DEAE-Sepharose and characterized with a wide range of optimized pH values (pH 4.5-9.0), temperature optima (40-60 °C), low Km values (2.27-3.3 mg/mL), and high substrate specificity toward large substrates. In conclusion, this study presents an efficient and green approach for utilization of agro-waste for production of the valuable α-amylase enzyme using RSM under SSF. RSM was particularly beneficial for the optimization and analysis of the effective process parameters.
固态发酵生产真菌淀粉酶被认为是满足全球酶市场不断增长需求的最佳商业规模方法。在这里,响应面法(RSM)用于优化固态发酵(SSF)中利用西瓜皮生产里氏木霉α-淀粉酶的过程变量。该统计模型包括四个变量,每个变量检测两个水平,然后进行部分纯化和α-淀粉酶特性分析。对部分纯化的α-淀粉酶进行了最佳 pH 值、温度、动力学常数和底物特异性的特性分析。结果表明,在 3 天的培养时间、水分含量为 50%、30°C 和 pH6.98 的优化工艺条件下,pH 值和水分含量都对α-淀粉酶的生产(880 U/g)有显著影响(P < 0.05)。RSM 的统计优化显示 R 值为 0.9934,证明了模型的有效性。使用 DEAE-琼脂糖分离出 5 种α-淀粉酶,并具有广泛的优化 pH 值(pH4.5-9.0)、最佳温度(40-60°C)、低 Km 值(2.27-3.3mg/mL)和对大底物的高底物特异性。总之,本研究提出了一种利用 RSM 在 SSF 下利用农业废物生产有价值的α-淀粉酶的有效且绿色的方法。RSM 特别有利于优化和分析有效的工艺参数。