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计算方法鉴定、表征、三维结构建模和基于机器学习的烟曲霉木聚糖酶耐热性预测。

Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus.

机构信息

Department of Biotechnology, National Institute of Technology Durgapur-713209, West Bengal, India.

Department of Biophysics, Univeristy of Calcutta-73209, West Bengal, India.

出版信息

Comput Biol Chem. 2021 Apr;91:107451. doi: 10.1016/j.compbiolchem.2021.107451. Epub 2021 Feb 6.

Abstract

Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because of its biotechnological relevance in many industries, such as pulp, paper, and bioethanol. In this study, we have identified and characterized xylanases from the genome of the thermophilic fungus of Aspergillus fumigatus by in silico analysis. Genome data mining revealed that the A fumigatus genome has six xylanase genes that belong to GH10, GH11, GH43 glycoside hydrolase families. In general, most of the bacterial and fungal GH11 xylanases are alkaline, and GH10 xylanases are acidic; however, we found that one identified xylanase from A fumigatus that belongs to the GH10 family is alkaline while the rest are acidic. Moreover, physicochemical properties also stated that most of the xylanases identified have lower molecular weight except one that belongs to the GH43 family. Structure prediction by homology modelling gave optimized structures of the xylanases. It suggests that GH10 family structure models adapt (β∕α) 8 barrel type, GH11 homology models adapt β-jelly type, and the GH43 family has a fivefold β-propeller type structure. Molecular docking of identified xylanases with xylan revealed that GH11 xylanases have strong interaction (-9.6 kcal/mol) with xylan than the GH10 (-8.5 and -9.3 kcal/mol) and GH43 (-8.8 kcal/mol). We used the machine learning approach based TAXyl server to predict the thermostability of the xylanases. It revealed that two GH10 xylanases and one GH11 xylanase are thermo-active up to 75ᵒC. We have explored the physiochemical properties responsible for maintaining thermostability for bacterial and fungal GH10 and GH11 xylanases by comparing crystal structures. All the analyzed parameters specified that GH10 xylanases from both the fungi and bacteria are more thermostable due to higher hydrogen bonds, salt bridges, and helical content.

摘要

从不同真菌和细菌物种中鉴定出耐热和碱性木聚糖酶,因其在许多行业(如纸浆、造纸和生物乙醇)中的生物技术相关性而引起了研究人员的关注。在这项研究中,我们通过计算机分析从嗜热真菌烟曲霉的基因组中鉴定和表征了木聚糖酶。基因组数据挖掘表明,烟曲霉基因组有六个木聚糖酶基因,属于 GH10、GH11 和 GH43 糖苷水解酶家族。一般来说,大多数细菌和真菌 GH11 木聚糖酶是碱性的,而 GH10 木聚糖酶是酸性的;然而,我们发现从烟曲霉中鉴定出的一种属于 GH10 家族的木聚糖酶是碱性的,而其余的是酸性的。此外,物理化学性质也表明,除了属于 GH43 家族的一个外,大多数鉴定出的木聚糖酶的分子量都较低。同源建模的结构预测给出了木聚糖酶的优化结构。这表明 GH10 家族结构模型适应(β∕α)8 桶型,GH11 同源模型适应β-果冻型,而 GH43 家族具有五倍β-推进器型结构。鉴定出的木聚糖酶与木聚糖的分子对接表明,GH11 木聚糖酶与木聚糖的相互作用较强(-9.6 kcal/mol),而 GH10(-8.5 和-9.3 kcal/mol)和 GH43(-8.8 kcal/mol)。我们使用基于 TAXyl 服务器的机器学习方法来预测木聚糖酶的热稳定性。结果表明,两种 GH10 木聚糖酶和一种 GH11 木聚糖酶在 75℃下具有热活性。我们通过比较晶体结构,探索了维持细菌和真菌 GH10 和 GH11 木聚糖酶热稳定性的理化性质。所有分析的参数都表明,由于氢键、盐桥和螺旋含量较高,真菌和细菌的 GH10 木聚糖酶更耐热。

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