SOURCE-LINKED INTELLIGENCE
Digging Deep into the Sequence Space of Electrochemical Debonding of Peptides to Impact Next Generation Polymer Adhesives
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 di
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 2499995
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T04:21:15.460Z. This is not the publication date.