Improvement of Heterologous Soluble Expression of L‐amino Acid Oxidase Using Logistic Regression

Author:

Nakahara Ayuta1,Su Zhengyu1,Wakayama Mamoru1,Nakamura Masaki2,Sakakibara Kazutoshi2,Matsui Daisuke13ORCID

Affiliation:

1. Department of Biotechnology College of Life Sciences Ritsumeikan University 1-1-1 Noji-higashi Kusatsu Shiga 525-8577 Japan

2. Department of Electrical and Computer Engineering Toyama Prefectural University 5180 Kurokawa Imizu Toyama 939-0398 Japan

3. Current address: Department of Applied Chemistry and Bioscience Chitose Institute of Science and Technology 758-65 Bibi Chitose Hokkaido 066-8655 Japan.

Abstract

AbstractSuccessful implementation of enzymes in practical application hinges on the development of efficient mass production techniques. However, in a heterologous expression system, the protein is often unable to fold correctly and, thus, forms inclusion bodies, resulting in the loss of its original activity. In this study, we present a new and more accurate model for predicting amino acids associated with an increased L‐amino acid oxidase (LAO) solubility. Expressing LAO from Rhizoctonia solani in Escherichia coli and combining random mutagenesis and statistical logistic regression, we modified 108 amino acid residues by substituting hydrophobic amino acids with serine and hydrophilic amino acids with alanine. Our results indicated that specific mutations in Euclidean distance, glycine, methionine, and secondary structure increased LAO expression. Furthermore, repeated mutations were performed for LAO based on logistic regression models. The mutated LAO displayed a significantly increased solubility, with the 6‐point and 58‐point mutants showing a 2.64‐ and 4.22‐fold increase, respectively, compared with WT‐LAO. Ultimately, using recombinant LAO in the biotransformation of α‐keto acids indicates its great potential as a biocatalyst in industrial production.

Funder

Asahi Glass Foundation

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3