SOURCE-LINKED INTELLIGENCE

Decoding the Biochemistry of Terpene Synthases

CORDIS · observation · Publication date unknown

sis, have yielded significant success over decades. Building on these foundations, the TerpenCode project aims to instantly elucidate and engineer enzymatic reactions by designing a new generation of deep learning models that (1) incorporate biochemical principles as inductive biases and (2) model all intermediate biochemical transformations that occur sequentially in the active site of each enzyme. We will focus on terpene synthases, which produce the core hydrocarbon scaffolds of terpenoids, the largest and most diverse class of natural products. My group has already curated a comprehensive training dataset comprising thousands of terpene synthase reaction mechanisms. In Objective O1, we will develop deep learning models for predicting the substrates, products, and reaction mechanisms of terpene synthases directly from their amino acid sequences. In Objective O2, we propose to engineer a generative machine learning algorithm for designing new variants of terpene synthases with altered quantitative product distribution, adjusted product stereochemistry, or new reaction cascades that

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recordType

award

status

SIGNED

region

EU

value

2158732.5

unit

EUR