Researchers at the University of Toronto have developed an AI system that can create proteins not found in nature using generative diffusion. The system will help advance the field of generative biology, making the design and testing of entirely new therapeutic proteins more efficient and flexible. The proteins are made from chains of amino acids that fold into three-dimensional shapes which dictate protein function. With a better understanding of how existing proteins fold, researchers have begun to design folding patterns not produced in nature. The new system uses a combination of biophysics-based representations of protein structure with diffusion methods from the image generation space to address the challenge of predicting which folds will be real and work in a protein structure.
Researchers at the University of Toronto have developed an artificial intelligence system that can create proteins not found in nature using generative diffusion, the same technology behind popular image-creation platforms such as DALL-E and Midjourney.
The system will help advance the field of generative biology, which promises to speed drug development by making the design and testing of entirely new therapeutic proteins more efficient and flexible.
“Our model learns from image representations to generate fully new proteins, at a very high rate,” says Philip M. Kim, a professor in the Donnelly Centre for Cellular and Biomolecular Research at U of T’s Temerty Faculty of Medicine. “All our proteins appear to be biophysically real, meaning they fold into configurations that enable them to carry out specific functions within cells.”
Today, the journal Nature Computational Science published the findings, the first of their kind in a peer-reviewed journal. Kim’s lab also published a pre-print on the model last summer through the open-access server bioRxiv, ahead of two similar pre-prints from last December, RF Diffusion by the University of Washington and Chroma by Generate Biomedicines.
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Proteins are made from chains of amino acids that fold into three-dimensional shapes, which in turn dictate protein function. Those shapes evolved over billions of years and are varied and complex, but also limited in number. With a better understanding of how existing proteins fold, researchers have begun to design folding patterns not produced in nature.
But a major challenge, says Kim, has been to imagine folds that are both possible and functional. “It’s been very hard to predict which folds will be real and work in a protein structure,” says Kim, who is also a professor in the departments of molecular genetics and computer science at U of T. “By combining biophysics-based representations of protein structure with diffusion methods from the image generation space, we can begin to address this problem.”
The new system, developed by Kim and his colleagues, uses generative diffusion to create new proteins that are biophysically real, meaning they fold into configurations that enable them to carry out specific functions within cells. This approach allows for the rapid creation of large numbers of proteins that can be tested for therapeutic potential.
The researchers believe that their system could be used to create entirely new classes of proteins that could be used to treat a range of diseases, from cancer to Alzheimer’s. They are currently working on improving the accuracy and speed of the system, as well as exploring new applications for generative biology.
In the long run, the development of this new AI system represents a major advance in the field of protein design and drug development. By enabling researchers to rapidly create and test new proteins with specific functions, it has the potential to revolutionize the way we approach the treatment of a wide range of diseases.
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