权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Biocatalytic data from enzymatic cascade reactions: integration of data acquisition, data mining, and mechanistic modeling

来自酶级联反应的生物催化数据：数据采集、数据挖掘和机械建模的集成

基本信息

批准号：
345504093
负责人：
Professor Dr. Jürgen Pleiss
金额：
--
依托单位：
Institut für Technische Chemie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/345504093?language=en
关键词：
Biocatalytic data enzymatic cascade reactions

项目摘要

Our project proposal complements the ongoing project of Dr. Selin Kara since 4/2016. The two projects are closely integrated and the project plan is agreed by both partners. By applying our BioCatNet database system, our project expands the project of Dr. Kara by two work packages: (1) Acquisition, storage and analysis of large datasets from biocatalytic experiments, (2) removal of reaction bottlenecks by optimization of the biocatalysts.BioCatNet is a database system for enzyme families integrating protein sequences, structures and biocatalytic data. The BioCatNet system allows for an acquisition of experimental data in a standardized and consistent manner, its exchange with other groups, as well as long-term archiving, thus making the data accessible to a later analysis by alternative models or other research groups.We discriminate between original data (time courses of substrates, intermediates and products) and derived, model-based data (kinetic parameters depending on a chosen kinetic model). The method, which will be developed within the proposed project, acquires data from enzymatic cascade reactions of Baeyer-Villiger monooxygenases and alcohol dehydrogenases and can be transferred to further biocatalytic reactions.The project of Dr. Kara aims at optimizing reaction conditions. In the framework of the proposed project, the next optimization step, the removal of enzyme-dependent reaction bottlenecks, will be prepared. Potential targets for this next optimization step are the stability of the applied Baeyer-Villiger monooxygenase under the chosen reaction conditions and the ratio between oxidation and reduction kinetics of the dehydrogenase, each of which are to be increased. Biocatalysts with the desired properties will be selected and developed by a systematic analysis of protein family databases and molecular modeling of substrate binding. Variable positions and positions involved in substrate binding are identified by a systematic comparison of sequences from the respective enzyme family.The proposed project contributes to the integration of data mining, kinetic and molecular modeling and establishes a concise process for the sustainable management of research data. We expect this process to significantly facilitate the handling and usage of biocatalytic data and to support their broad usage.

我们的项目提案是对Selin Kara博士自2016年4月以来正在进行的项目的补充。这两个项目紧密结合在一起，项目计划由双方合作伙伴商定。通过应用我们的BioCatNet数据库系统，我们的项目将Kara博士的项目扩展为两个工作包：(1)从生物催化实验中获取、存储和分析大型数据集，(2)通过优化生物催化剂来消除反应瓶颈。BioCatNet是一个集成了蛋白质序列、结构和生物催化数据的酶家族数据库系统。BioCatNet系统允许以标准化和一致的方式获取实验数据，与其他组交换数据，并进行长期存档，从而使数据可供替代模型或其他研究小组进行后续分析。我们区分原始数据(底物、中间体和产品的时间进程)和基于模型的派生数据(动力学参数取决于所选的动力学模型)。该方法将在拟议的项目中开发，从Baeyer-Villiger单加氧酶和乙醇脱氢酶的酶促级联反应中获得数据，并可以转移到进一步的生物催化反应中。Kara博士的项目旨在优化反应条件。在拟议项目的框架内，将准备下一个优化步骤，即消除酶依赖反应瓶颈。下一个优化步骤的潜在目标是所应用的Baeyer-Villiger单加氧酶在所选择的反应条件下的稳定性以及脱氢酶的氧化和还原动力学之间的比率，这两个指标都有待提高。通过对蛋白质家族数据库的系统分析和底物结合的分子模拟，将选择和开发具有所需性质的生物催化剂。通过对各个酶家族的序列进行系统比较，确定了底物结合的可变位置和位置。建议的项目有助于数据挖掘、动力学和分子建模的集成，并为研究数据的可持续管理建立了一个简洁的过程。我们预计这一过程将极大地促进生物催化数据的处理和使用，并支持它们的广泛使用。