New deep-learning model outperforms Google AI system in predicting peptide structures

Peptides are known to be highly flexible, taking on a wide range of folding patterns, and are thus involved in many biological processes of interest to researchers in the development of therapeutics.

"Peptides were the focus of the PepFlow model because they are very important biological molecules and they are naturally very dynamic, so we need to model their different conformations to understand their function," said, principal investigator on the study and a professor at the Donnelly Centre."They're also important as therapeutics, as can be seen by the GLP1 analogues, like Ozempic, used to treat diabetes and obesity.

What sets PepFlow apart is the technological innovations that power it. For instance, it is a generalized model that takes inspiration from Boltzmann generators, which are highly advanced physics-based machine learning models. PepFlow was built to be easily expanded to account for additional considerations and new information and potential uses. Even as a first version, PepFlow is a comprehensive and efficient model with potential for furthering the development of treatments that depend on peptide binding to activate or inhibit biological processes.

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Researchers develop deep-learning model that outperforms Google AI system to predict peptide structuresResearchers at the University of Toronto have developed a deep-learning model, called PepFlow, that can predict all possible shapes of peptides—chains of amino acids that are shorter than proteins, but perform similar biological functions.
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