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Decoding the Biochemistry of Terpene Synthases

Project description

Enzyme design with deep learning

Enzymes play a vital role in biotechnology by catalysing complex reactions, yet designing and optimising them remains a challenging and slow process. The ERC-funded TerpenCode project is changing that by leveraging deep learning to model enzymatic reactions with greater precision. By focusing on terpene synthases, which produce the building blocks of terpenoids, the project aims to predict how enzymes function from their amino acid sequences. This approach will allow researchers to engineer new enzyme variants, potentially creating novel products with valuable applications. Ultimately, TerpenCode’s advancements could revolutionise enzyme design, enabling faster, more sustainable biotechnological solutions and opening the door to entirely new chemical pathways.

Objective

Enzymes are biological catalysts indispensable for biotechnology. Conventional approaches to enzyme design and optimization, relying on biochemical intuition and combinatorial mutagenesis, 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 lead to novel terpene products. We will experimentally validate these models by yeast expression experiments, including complete chemical structure elucidation of the detected reaction products. Breakthrough progress on these objectives would be a key important step towards the holy grail of biotechnology: providing a computational prediction of the exact enzyme function from its amino acid sequence and instant de novo generation of new enzymes for catalyzing desired biochemical reactions for an important class of enzymes. Generalizing our solutions further to other classes of enzymes would enable sustainable biotechnological production of a broad spectrum of new-to-nature chemicals and bioactives.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-COG

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Host institution

USTAV ORGANICKE CHEMIE A BIOCHEMIE, AV CR, V.V.I.
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 158 732,50
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 158 732,50

Beneficiaries (1)

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