MIT researchers are using artificial intelligence to design new proteins that go beyond those found in nature.
They developed machine-learning algorithms that can generate proteins with specific structural features, which could be used to make materials that have certain mechanical properties, like stiffness or elasticity. Such biologically inspired materials could potentially replace materials made from petroleum or ceramics, but with a much smaller carbon footprint.
The researchers from MIT, the MIT-IBM Watson AI Lab, and Tufts University employed a generative model, which is the same type of machine-learning model architecture used in AI systems like DALL-E 2. But instead of using it to generate realistic images from natural language prompts, like DALL-E 2 does, they adapted the model architecture so it could predict amino acid sequences of proteins that achieve specific structural objectives.
In a paper published today in Chem, the researchers demonstrate how these models can generate realistic, yet novel, proteins. The models, which learn biochemical relationships that control how proteins form, can produce new proteins that could enable unique applications, says senior author Markus Buehler, the Jerry McAfee Professor in Engineering and professor of civil and environmental engineering and of mechanical engineering.
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