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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesmathematicspure mathematicstopology
- natural scienceschemical sciencespolymer sciences
- natural scienceschemical sciencesorganic chemistryamines
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
61 712 Poznan
Poland