The approach could expedite the antibody-creation process and accelerate the discovery of therapeutic antibody candidates and vaccines.
An invaluable tool in the discovery and development of new therapies, "antibody libraries" are collections of genetically engineered antibodies used in research and industry to discover and develop therapies for viruses, cancer, and other diseases. A team of Johns Hopkins scientists and engineers has designed a new approach to generating antibody libraries that could not only expedite the antibody-creation process, but also accelerate the discovery of therapeutic antibody candidates, minimize risks to proper immune response, and make the entire process less costly and time consuming.
"These libraries are usually generated by engineers randomly mutating sequences. The result is that not every antibody generated is going to work or be well behaved in the body. Our approach is different: We used a deep-learning, artificial intelligence model to create high-quality libraries on demand," said team leader Jeffrey Gray, professor in the Department of Chemical and Biomolecular Engineering at Johns Hopkins University's Whiting School of Engineering and an associate in the Institute for NanoBioTechnology.
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The development of their new tool "Immunoglobulin Language Model," or IgLM, was trained on a set of half a billion real human antibody sequences. The team envisions that this tool will enable faster discovery of therapeutic antibodies. They are now seeking a partner to move into experimental testing.
- Caption: Johns Hopkins Jeffrey Gray ecosystem
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