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EHreact:用于提取和评分酶反应模板的扩展哈塞图。

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates.

机构信息

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

出版信息

J Chem Inf Model. 2021 Oct 25;61(10):4949-4961. doi: 10.1021/acs.jcim.1c00921. Epub 2021 Sep 29.

Abstract

Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply, and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

摘要

在过去十年中,利用大型数据库中的有机或生物催化反应进行数据驱动的计算机辅助合成规划引起了越来越多的关注,激发了许多工具的发展,用于提取、应用和评分通用反应模板。酶反应的反应规则的生成尤其具有挑战性,因为酶之间的底物混杂性不同,导致规则特异性的最佳水平和包含的原子数量的最佳数量在酶之间有所不同。这使得从数据库中自动提取变得复杂,并促进了手动策管的反应规则集的创建。在这里,我们提出了 EHreact,这是一个纯粹的数据驱动的开源软件工具,用于从一组已知由酶以适当的特异性水平催化的反应中提取和评分反应规则,而无需专家知识。EHreact 根据想象的过渡结构中的常见亚结构将反应规则提取并分组到树状结构、哈塞图中。每个图都可以用于输出单个或一组反应规则,以及通过从已知反应及其在模板树中的分组中推断有关酶的反应位点的信息来计算新底物被给定酶处理的概率。EHreact 启发式地预测给定酶对新底物的活性,在准确性和功能方面优于当前方法。

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