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Digging Deep into the Sequence Space of Electrochemical Debonding of Peptides to Impact Next Generation Polymer Adhesives

Project description

Digital strategies to identify and engineer next-gen electroswitchable polymers

Adhesives are everywhere. They are in phones, medical devices, and construction. However, once bonded, most adhesives are difficult or impossible to remove without damage. This limits recycling, repair, and reuse, wasting both materials and energy. What if there were a way to design glues that attach firmly when needed, but release cleanly on command? The ERC-funded IDefix project aims to make this possible. By combining peptide chemistry, high-throughput sequencing, and machine learning, it deciphers how polymers interact with surfaces to create tunable, ‘intelligent’ adhesives. The team’s data-driven design platform, SurPhage, identifies molecular structures that respond to electric or mechanical triggers, paving the way for reversible coatings, self-healing composites, and even remote-controlled medical patches.

Objective

Generating novel polymer functions based on rational design criteria, derived from deep statistical analysis of massive experimental data sets, would fundamentally impact materials development and represent the next evolutionary step in macromolecular engineering.


The IDefix project establishes a generic information-based design strategy to define polymer–surface interactions that represent the key property in various applications. The project focuses on advancing adhesives, to facilitate material-specific adsorption and triggerable desorption by distinct electrochemical transformations.

The rationale of engineering such polymers is extracted from peptide phage display (PD) biopanning with an advanced selection scenario and using next-generation sequencing (NGS). This allows to screen 10^9 sequences and readout of 10^6, providing the data sets to feed machine learning (ML) tools.
A new software tool “SurPhage” is developed and tailored to the material-oriented biopanning. Leveraging ML concepts, sequence data interpretation and feature abstraction are combined with sequence-function data of a broad analysis pipeline to learn on the rationale that feeds generative models for in-silico design.

The underlying chemistry relies on peptides with L-3,4-Dihydroxyphenylalanin (Dopa)-residues that show potent catechol anchors and a unique debonding mechanism on quinone oxidation. However, the strategy enables to identify hidden champions and discover novel Dopa-free mechanisms.

Employing the design rationale an IDefix platform is developed, covering polymers from artificial adhesive proteins to copolymers. These enable the electrochemical manipulation of adhesives, coatings or membranes, facilitating applications of debonding on command or dimming of permeability. Combining IDefix materials with piezoelectric elements leads to self-reinforcing mechano-responsive composites and integrating in near-field communication devices enables remote controlled drug release patches.

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Call for proposal

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(opens in new window) ERC-2024-ADG

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Host institution

HUMBOLDT-UNIVERSITAET ZU BERLIN
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.

€ 2 499 995,00
Address
UNTER DEN LINDEN 6
10117 Berlin
Germany

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Region
Berlin Berlin Berlin
Activity type
Higher or Secondary Education Establishments
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Total cost

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Beneficiaries (1)

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