Objective
Peptide-based drugs offer distinct advantages over existing molecular therapeutics, such as their ability to engage targets that have proven challenging for small molecules, and their capability to penetrate cells and modulate intracellular targets, which conventional biologics cannot achieve. However, despite this potential, the development of peptide-based medicines has lagged due to the lack of high-quality, large-scale protein-peptide interaction datasets essential for rational AI-driven peptide design. During the ERC Consolidator Grant DiProPhys, we developed a platform that can profile protein/peptide interactions at scale. As part of the PepVerse project, we will leverage this platform to generate proof-of-concept data for commercial exploitation. Specifically, we will generate binding data for three high-value therapeutic targets that currently lack orally available drugs due to difficulties in identifying non-peptidomimetic small molecule binders. These data will not only validate the platform’s ability to discover binders but will also provide specific starting points for high-value drug discovery programmes. Moreover, the data will showcase the platform’s potential to create the world’s largest protein/peptide mimotope dataset foundational for advancing any peptide-based drug discovery programme --- a goal we aim to achieve with the help of private capital. In addition to generating these validation data, which are key for fundraising and translating the technology to a commercial entity, our other critical objectives for the project include establishing solid IP protection for the technology and building relationships with key stakeholders, such as advanced library providers and pharma partners. By the end of the project, we aim to have generated robust validation data, filed an intellectual property application, and raised funds to launch a peptide-based drug discovery company
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. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC-POC - HORIZON ERC Proof of Concept GrantsHost institution
CB2 1TN Cambridge
United Kingdom