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Evolution of shape-defined macromolecules into functional systems

Project description

Optimisation of catalytic function by sequence regulation using machine learning

Enzymes are biological catalysts that accelerate chemical reactions within living organisms, enabling substrates to be converted into products efficiently with lower energy expenditure. Each enzyme is highly specific to its substrate and has evolved to operate under physiological conditions. The ERC-funded SHAPE project aims to develop abiotic enzymes with catalytic capabilities comparable to natural ones, expanding their use in chemical transformations. The work will focus on varying monomer sequence and stereochemistry to alter secondary and tertiary structure in a controlled engineering approach. Advanced computational techniques will help analyse and interpret the relationship between sequence and catalytic function, leading to optimised catalytic performance.

Objective

Functionalities of enzymes are encoded in amino acid sequences and directed by their SHAPEs with complementary binding pockets for specific substrates. Natural enzymes are remarkable catalysts, however, they are typically optimized by evolution to operate under the constraints of the physiological environment of a living system, which strongly limits the scope of their applications in organic synthesis. Here, I propose to develop abiotic enzymes to selectively catalyze chemical transformations in non-physiological environments. The main objective of the project is to use monomer sequence control to fine-tune the SHAPE of abiotic macromolecules to obtain the desired catalytic functionality. This goal will be realised via four work packages:
(I) Primary structure control to input information into macromolecules – development of synthetic methods yielding high molar mass, sequence-defined polymers, to deliver abiotic proteins at high scales and numbers.
(II) SHAPE control by single chain folding and topology – secondary and tertiary structure evolution by varying the monomer sequence and stereochemistry to tune intramolecular interactions, leading to controlled engineering of globularly folded polymers.
(III) Introducing catalytic activity into abiotic polymers – enhancing selectivity and efficiency of catalytic reactions by advancing an outer sphere that surrounds the metal cofactor.
(IV) Sequence-function studies using machine learning – delivery of models able to interpret multivariate data that will guide the development of complex catalytic systems to find and predict dependencies inaccessible by conventional methods.
Our approach proposes an unexplored method for obtaining abiotic, sequence-defined polymers operating in a non-biological environment whose functions can rival those of natural macromolecules. The study will reveal valuable information on sequence-dependent properties of polymers, to open a field of abiotic enzymes for organic transformations.

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Call for proposal

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(opens in new window) ERC-2023-STG

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Host institution

UNIWERSYTET IM. ADAMA MICKIEWICZA WPOZNANIU
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.

€ 1 499 750,00
Address
ULICA HENRYKA WIENIAWSKIEGO 1
61 712 POZNAN
Poland

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Region
Makroregion północno-zachodni Wielkopolskie Miasto Poznań
Activity type
Higher or Secondary Education Establishments
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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.

€ 1 499 750,00

Beneficiaries (1)

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