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Product prediction of terpene synthase using graph neural network / グラフニューラルネットワークを用いたテルペンシンターゼの生成物予測
Document Type
Journal Article
Source
Proceedings of the Symposium on Chemoinformatics. 2019, :1
Subject
deep learning
graph neural network
terpenoid
グラフニューラルネットワーク
テルペノイド
深層学習
Language
Japanese
Abstract
Terpenoids are one of the main secondary metabolite groups and have a variety of physical properties and physiological activities, so they are widely used in fragrances, pharmaceuticals and fuels. In terpenoid biosynthesis, various terpene skeletons are formed from a common precursor by an enzyme called terpene synthase at an intermediate stage. Terpene synthases have reaction specificity, and each terpene synthase catalyzes the formation of a defined terpene skeleton. Therefore, understanding the relationship between the amino acid sequence and reaction characteristics is important in applications such as the biotechnological production of the desired terpenoid, but the correspondence is still unclear. Therefore, the correspondence relationship between the structure of the terpene skeleton and the amino acid sequence is examined by creating a neural network model that performs interconversion between the terpene skeleton and the amino acid sequence of the terpene synthase.

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