{"id":215334,"date":"2025-06-04T05:17:22","date_gmt":"2025-06-04T10:17:22","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2025\/06\/ai-used-to-design-immune-safe-zinc-finger-proteins-for-gene-therapy"},"modified":"2025-06-04T05:17:22","modified_gmt":"2025-06-04T10:17:22","slug":"ai-used-to-design-immune-safe-zinc-finger-proteins-for-gene-therapy","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2025\/06\/ai-used-to-design-immune-safe-zinc-finger-proteins-for-gene-therapy","title":{"rendered":"AI used to design immune-safe \u2018zinc finger\u2019 proteins for gene therapy"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-used-to-design-immune-safe-zinc-finger-proteins-for-gene-therapy2.jpg\"><\/a><\/p>\n<p>Machine learning models have seeped into the fabric of our lives, from curating playlists to explaining hard concepts in a few seconds. Beyond convenience, state-of-the-art algorithms are finding their way into modern-day medicine as a powerful potential tool. In one such advance, <a href=\"http:\/\/dx.doi.org\/10.1016\/j.cels.2025.101299\" target=\"_blank\">published<\/a> in <i>Cell Systems<\/i>, Stanford researchers are using machine learning to improve the efficacy and safety of targeted cell and gene therapies by potentially using our own proteins.<\/p>\n<p>Most human diseases occur due to the malfunctioning of proteins in our bodies, either systematically or locally. Naturally, introducing a new therapeutic protein to cure the one that is malfunctioning would be ideal.<\/p>\n<p>Although nearly all therapeutic protein antibodies are either fully human or engineered to look human, a similar approach has yet to make its way to other therapeutic proteins, especially those that operate in cells, such as those involved in CAR-T and CRISPR-based therapies. The latter still runs the risk of triggering immune responses. To solve this problem, researchers at the Gao Lab have now turned to machine learning models.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning models have seeped into the fabric of our lives, from curating playlists to explaining hard concepts in a few seconds. Beyond convenience, state-of-the-art algorithms are finding their way into modern-day medicine as a powerful potential tool. In one such advance, published in Cell Systems, Stanford researchers are using machine learning to improve the [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1902,11,41,6],"tags":[],"class_list":["post-215334","post","type-post","status-publish","format-standard","hentry","category-bioengineering","category-biotech-medical","category-information-science","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/215334","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=215334"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/215334\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=215334"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=215334"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=215334"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}