Comparative analysis of l-sorbose dehydrogenase by docking strategy for 2-keto-l-gulonic acid production in Ketogulonicigenium vulgare and Bacillus endophyticus consortium

Author:

Chen Si12,Jia Nan12,Ding Ming-Zhu12,Yuan Ying-Jin12

Affiliation:

1. grid.33763.32 0000000417612484 Key Laboratory of Systems Bioengineering (Ministry of Education) School of Chemical Engineering and Technology Tianjin University 300072 Tianjin People’s Republic of China

2. grid.33763.32 0000000417612484 SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) 300072 Tianjin People’s Republic of China

Abstract

Abstract Improving the yield of 2-keto-l-gulonic acid (2-KGA), the direct precursor of vitamin C, draws more and more attention in industrial production. In this study, we try to increase the 2-KGA productivity by computer-aided selection of genes encoding l-sorbose dehydrogenases (SDH) of Ketogulonicigenium vulgare. First, six SDHs were modeled by docking strategy to predict the binding mode with co-factor PQQ. The binding energy between SSDA1-H/SSDA1-L and PQQ was the highest, followed by SSDA3/SSDA2. The binding energy between SSDA1-P/SSDB and PQQ was the lowest. Then, these genes were overexpressed, respectively, in an industrial strain K. vulgare HKv604. Overexpression of ssda1-l and ssda1-h enhanced the 2-KGA production by 7.89 and 12.56 % in mono-cultured K. vulgare, and by 13.21 and 16.86 % when K. vulgare was co-cultured with Bacillus endophyticus. When the engineered K. vulgare SyBE_Kv000116013 (overexpression of ssda1-p) or SyBE_Kv000116016 (overexpression of ssdb) was co-cultured with B. endophyticus, the 2-KGA production decreased significantly. The docking results were in accordance with the experimental data, which indicated that computer-aided modeling is an efficient strategy for screening more efficient enzymes.

Funder

Ministry of Science and Technology of China

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Applied Microbiology and Biotechnology,Biotechnology,Bioengineering

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