Researchers at the lab of Nobel laureate David Baker have already developed protein minibinders<\/a> that perform a similar function to an antibody. But Robert Ragotte, one of the authors of the new paper, says artificial intelligence\u2013designed antibodies have a few advantages over the minibinders.<\/p>\n Because humans already make their own antibodies, antibodies are less likely to be immunogenic because the body is more familiar with them. Additionally, antibodies are already widely used in the pharmaceutical world, while the minibinders would be an entirely new modality. But antibodies have eluded AI protein design programs because of their complementarity-determining region loops, which are flexible. Flexibility introduces uncertainty into the design process, as \u201cthere isn\u2019t one true structure,\u201d Ragotte says.<\/p>\n The Baker Lab is not the only academic lab releasing new results in recent weeks. At the end of October, the Boltz team<\/a>, led by Massachusetts Institute of Technology professors Regina Barzilay and Tommi Jaakola, released its latest model.<\/p>\n BoltzGen<\/a>, developed by PhD student Hannes St\u00e4rk, is an all-atom generative model for designing proteins and peptides to bind a wide range of biomolecular targets.<\/p>\n \u201cThe emphasis here is on unsolved problems,\u201d Barzilay said on a call with reporters, which she joined straight from teaching a class on AI and drug discovery. That\u2019s why, she says, the Boltz team is focusing on finding solutions to currently undruggable targets. The work is described in a preprint<\/a> that has not yet been peer-reviewed.<\/p>\n
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