Dentin hypersensitivity (DH) is a prevalent health condition occurring in more than 50% of the population. Despite the upward trend of risk, there has not been a permanent solution to DH. An ideal treatment should reproduce a well-integrated mineral layer, mimicking the protective cementum tissue that covers exposed tubules and seals tightly by penetrating into them. Such restoration can be accomplished using peptides that epitomize mineralization properties of dental proteins. Proposed herein is the development of a novel approach called Deep Mineralization, that will enable biomimetic restoration of exposed dentin by forming a well-integrated acellular cementum-like protective mineral layer. This will be accomplished via four specific objectives that include: (i) Experimental selection of peptides via directed evolution paired with next-generation sequencing called Deep Directed Evolution to generate peptide datasets (big data); (ii) Development of protein-derived peptides via machine learning (ML) based predictive design platform (Biomineralization Intelligence) that is trained with big data and refined through (iii) High-throughput (HTP) validation assays, and eventually (iv) Establishing a peptide-guided remineralization methodology (Deep Mineralization) by mimicking the remineralization action of saliva in which remineralizing peptides bind to exposed dentin, recruit calcium and phosphate ions and synthesize an integrated acellular cementum-like mineral layer, thereby, restoring tooth’s natural protection. Aligned with the current vision of the EU that aims to adapt methodologies to generate big data and tools to enable fast-track discoveries with healthcare delivery, this study marks the onset of implementing ML and HTP screening methods into dental research. Successful completion of this project will result in a line of preventive oral care products with the long-term vision of establishing a molecular toolkit for complete tissue regeneration.
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