A group of scientists led by Joseph Brown, an assistant professor in the University of Toronto's Leslie Dan Faculty of Pharmacy, is developing an open-access database and artificial intelligence training model to identify peptides with therapeutic potential.
The new initiative - supported by US$2.3 million in funding through the Google.org Impact Challenge: AI for Science initiative - is called OpenPep and will allow researchers to use AI to predict which peptides will bind to target proteins.
"Peptides are having a moment: Many adults have tried a peptide drug, and eight per cent of newly approved drugs are peptides. They are no longer a niche class of drugs," says Brown, who is also a scientific adviser for the Acceleration Consortium , a U of T institutional strategic initiative focused on self-driving, AI-guided robotic labs.
"The real challenge is that we don't have a lot of existing data for peptide discovery, so developing peptide medicines is still remarkably slow. That challenge inspired us to gather large amounts of data so we can build chemically informed models and speed up the pace of discovery."
Peptides are short chains of amino acids, smaller than a protein but larger than a conventional small-molecule drug. When engineered, they can be stable, precise and selective in modulating biological targets, and they hold a lot of promise as drugs.
Yet, while researchers working in other areas of drug discovery can make use of AI models such as Google's AlphaFold - which is focused on protein structures and is built from open-source data developed over decades of research - to make predictions and speed up the research process, there are no similar databases and AI models for peptides. As a result, peptide discovery almost exclusively progresses through experiments, with data scattered across various labs, projects and companies.
To close this gap, Brown and his co-investigators - Keith Pardee, an associate professor in the Leslie Dan Faculty of Pharmacy; Philip Kim, a professor in U of T's Donnelly Centre for Cellular and Biomolecular Research; and U of T PhD alumnus Chris Ing, co-founder and chief scientific officer of ProteinQure - decided to team up to build a database and matching AI model for peptide binding that researchers around the world can use and contribute to.
"AI has the potential to transform how we understand biology and accelerate the discovery of new therapeutics," says Leslie Yeh, director of scientific progress at Google.org. "By pairing our targeted, early-stage funding with advanced AI, we can give researchers the runway to take bold risks on foundational science that traditional sources of capital may overlook. OpenPep aims to do just that - breaking down fragmented data barriers so scientists around the world can openly share knowledge and build on one another's discoveries.
"We're proud to support the University of Toronto in building this resource and advancing a more open, collaborative approach to AI-powered scientific discovery."
Research team will crowd-source suggestions of disease-relevant proteins to target
OpenPep leverages the expertise of the co-investigators across peptide discovery, design and machine learning to generate large amounts of data and build models to predict peptide-protein binding.
Pardee's group will draw on their expertise in accessible cell-free systems from synthetic biology to produce up to 500 disease-relevant proteins, a snapshot of the human proteome. Brown's team will use specialized mass spectrometry to experimentally test large libraries of peptides against the target proteins and measure which peptides bind and their binding strength.
The team will then use the experimental data to build and train AI models that can predict how well peptides bind to proteins that have not been tested experimentally. As the researchers test and validate those predictions, the data can be added to the model to further refine predictions.
"AI can turn thousands of individual peptide experiments into generalizable rules about how peptides interact with proteins," says Ing. "With OpenPep, the goal is to use those insights to predict and design peptides for targets we haven't experimentally explored yet, allowing the value of each experiment to extend well beyond the specific peptide and target that were measured."
As the team begins generating experimental data, they will crowd-source suggestions of proteins for peptide discovery from collaborators at hospitals, pharmaceutical companies and research institutes with particular interest in pediatric and rare diseases, cancer, neurodegeneration and cardiovascular disease. Any researcher interested in nominating a target for discovery will be able to submit suggestions via a webform .
Once established, OpenPep will be an open database that the team and other researchers can continue to contribute to further model improvements.
"Some of the biggest advances in biology have been built on open scientific resources. No single laboratory can explore the full diversity of peptide chemistry and disease biology, so making the resource open also gives it the potential to grow through contributions from the broader scientific community," says Ing.
"OpenPep will give peptide drug-discovery teams around the world a head start. Researchers will be able to build on a large body of experimental data to find and optimize promising peptide molecules more efficiently. Ultimately, we hope this makes peptide discovery more predictive and shortens the path from a disease target to a potential therapeutic."
Brown says the investment from the Google.org Impact Challenge award is key to ensuring the researchers are able to deliver on their goals.
"This funding gives us the momentum to deliver on committing to dedicated data generation efforts required to advance the field in this way," says Brown. "The scale of data that we're going to put together doesn't exist anywhere else, and this support is going to help us lead in the space and unlock a completely different AI-powered paradigm for peptide discovery."
"AI won't replace experiments, but it will help us choose better ones by identifying the peptides most worth testing," says Brown, with the team noting that "by turning experiments into open data, and open data into AI, we hope to give more scientists a better place to start - and accelerate the discovery of new peptides for the benefit of everyone."
