Through research funded partially by the MIT Jameel Clinic, researchers at MIT have developed a computational approach that helps predict mutations that will lead to better, optimised proteins for desirable functions, based on a relatively small amount of data. Regina Barzilay, AI faculty lead at the MIT Jameel Clinic, and Tommi Jaakkola, an MIT Jameel Clinic principal investigator, are senior authors of an open-access paper on the work, which will be presented at the International Conference on Learning Representations in May.
Scientists usually put natural proteins through many rounds of randomized mutation to produce an optimized protein. This has resulted in an optimized version of the key protein, green fluorescent protein (GFP), but the process is not always successful for all proteins. Using the computational approach, researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.
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To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.
This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.
Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.