IMPACT CASE STUDY:
Structural modelling of natural peptide target interactions
Prof Denis Shields (UCD)
Dr Clement Agoni (UCD)
Dr Hansel Gómez (Nuritas)
#Bioactive peptides, machine learning, discovery, molecular modelling
Nuritas is disrupting the industry of natural ingredients by putting consumers first. Through pioneering life science and artificial intelligence, they developed a unique Nuritas MagnifierNπΦ platform that allows the company to identify, unlock, clinically test, and patent peptides from natural sources that have remained disregarded by science until today. This entire process is consumer centric and starts with identifying the desired efficacy. Developing preventative and curative peptide-based treatments for disease can be challenging because peptides adopt more flexible structures than small molecules. This project seeks to understand whether structural modelling can accelerate the development of peptides from natural sources for therapeutic purposes while also improving our understanding of their mechanisms of action.
The Commercial opportunity
Naturally sourced bioactive peptides have strong safety profiles and easier regulatory approval as they can be derived from well characterised food proteins. The bioactive peptide market remains underexploited due to the complexity required in discovering and characterising these molecules.
The Innovative solution
The research team are assembling a novel machine learning pipeline to identify and prioritise naturally occurring peptides and test their binding to therapeutic targets using molecular modelling techniques.
The Research Impact
The outputs from this project will be used to train new machine learning models to improve the discovery efficiency of Nuritas technology and will positively impact the company’s capacity to uncover novel health-promoting peptides.