Designing synthetic proteins that can act as drugs for cancer or other diseases can be a tedious process: It generally involves creating a library of millions of proteins, then screening the library to find proteins that bind the correct target.
MIT biologists have now come up with a more refined approach in which they use computer modeling to predict how different protein sequences will interact with the target. This strategy generates a larger number of candidates and also offers greater control over a variety of protein traits, says Amy Keating, a professor of biology and biological engineering and the leader of the research team.
“Our method gives you a much bigger playing field where you can select solutions that are very different from one another and are going to have different strengths and liabilities,” she says. “Our hope is that we can provide a broader range of possible solutions to increase the throughput of those initial hits into useful, functional molecules.”
In a paper appearing in the Proceedings of the National Academy of Sciences, Keating and her colleagues used this approach to generate several peptides that can target different members of a protein family called Bcl-2, which help to drive cancer growth.
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