{"id":8436,"date":"2023-12-19T16:35:20","date_gmt":"2023-12-19T16:35:20","guid":{"rendered":"https:\/\/dailyai.com\/?p=8436"},"modified":"2023-12-19T16:35:20","modified_gmt":"2023-12-19T16:35:20","slug":"ai-designed-proteins-display-exceptional-binding-strengths","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/","title":{"rendered":"Las prote\u00ednas dise\u00f1adas con IA presentan una fuerza de uni\u00f3n excepcional"},"content":{"rendered":"<p><strong>Cient\u00edficos de la Facultad de Medicina de la Universidad de Washington utilizaron la IA para dise\u00f1ar nuevas mol\u00e9culas prote\u00ednicas de un modo que abre interesantes posibilidades para la detecci\u00f3n y el tratamiento de enfermedades.<\/strong><\/p>\n<p>La eficacia de un f\u00e1rmaco en el tratamiento de una enfermedad depende en gran medida de su especificidad a la hora de adherirse a la diana causante de la enfermedad y de la fuerza con que se adhiera a ella.<\/p>\n<p>La cantidad casi infinita de formas potenciales de prote\u00ednas hace que dise\u00f1ar una prote\u00edna con alta afinidad (fuerza de uni\u00f3n) y alta especificidad sea una tarea extremadamente dif\u00edcil.<\/p>\n<p>Encontrar prote\u00ednas que se unan fuertemente a objetivos pept\u00eddicos helicoidales como el glucag\u00f3n, el neurop\u00e9ptido Y y la hormona paratiroidea era la dif\u00edcil tarea en la que los investigadores esperaban que la IA pudiera ayudarles. Su art\u00edculo, <a href=\"https:\/\/www.nature.com\/articles\/s41586-023-06953-1\" target=\"_blank\" rel=\"noopener\">publicado en Nature<\/a>muestra el potencial de la IA para revolucionar <a href=\"https:\/\/dailyai.com\/es\/2023\/12\/astrazeneca-bets-247m-on-ai-company-developing-cancer-drug\/\">desarrollo de f\u00e1rmacos<\/a>.<\/p>\n<p>Los investigadores utilizaron por primera vez m\u00e9todos de alucinaci\u00f3n de aprendizaje profundo con AlphaFold2 y RosettaFold. Estos programas avanzados de IA han sido de gran ayuda para dise\u00f1ar nuevas prote\u00ednas, pero consumen mucha potencia de c\u00e1lculo.<\/p>\n<p>En un esfuerzo por encontrar un m\u00e9todo de dise\u00f1o de prote\u00ednas m\u00e1s eficiente desde el punto de vista computacional, los investigadores recurrieron a un m\u00e9todo similar al que utilizan modelos de generaci\u00f3n de im\u00e1genes de inteligencia artificial como Stable Diffusion y DALL-E.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Papel de hoy <a href=\"https:\/\/twitter.com\/Nature?ref_src=twsrc%5Etfw\">@Nature<\/a> describe <a href=\"https:\/\/twitter.com\/UWproteindesign?ref_src=twsrc%5Etfw\">@UWproteindesign<\/a> Prote\u00edna generada por IA con una afinidad y especificidad de uni\u00f3n excepcionalmente altas. Esta biotecnolog\u00eda tiene implicaciones para el desarrollo de f\u00e1rmacos, la detecci\u00f3n de enfermedades y la vigilancia del medio ambiente. <a href=\"https:\/\/t.co\/KhtGhiyf6I\">https:\/\/t.co\/KhtGhiyf6I<\/a><\/p>\n<p>- UW Medicine Newsroom (@uwmnewsroom) <a href=\"https:\/\/twitter.com\/uwmnewsroom\/status\/1736785659137016153?ref_src=twsrc%5Etfw\">18 de diciembre de 2023<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<h2>RFdifusi\u00f3n<\/h2>\n<p>RFdiffusion es un innovador modelo generativo de inteligencia artificial que se entrena con datos de estructuras proteicas conocidas. A continuaci\u00f3n, el modelo refina y reorganiza iterativamente los \u00e1tomos en estructuras proteicas definidas.<\/p>\n<p>RFdiffusion se entren\u00f3 para eliminar iterativamente el ruido de las nubes de \u00e1tomos desconectados y, a continuaci\u00f3n, reorganizarlas en nuevas estructuras proteicas. Este modelo se utiliz\u00f3 junto con la herramienta de software ProteinMPNN, dise\u00f1ada por el laboratorio Baker, que forma parte del proyecto <a href=\"https:\/\/www.ipd.uw.edu\/\" target=\"_blank\" rel=\"noopener\">Instituto de Dise\u00f1o de Prote\u00ednas<\/a> en la Universidad de Washington.<\/p>\n<p>ProteinMPNN toma una estructura proteica como entrada y utiliza t\u00e9cnicas de aprendizaje profundo para identificar r\u00e1pidamente nuevas secuencias de amino\u00e1cidos que probablemente se plieguen en estructuras proteicas espec\u00edficas.<\/p>\n<h2>Resultados apasionantes<\/h2>\n<p>Las prote\u00ednas dise\u00f1adas por los investigadores mostraron una afinidad y especificidad excepcionalmente altas con los p\u00e9ptidos a los que se dirig\u00edan. Esto significa que podr\u00edan utilizarse para crear f\u00e1rmacos dirigidos a la causa de una enfermedad, en lugar de unirse a dianas no deseadas y provocar efectos secundarios.<\/p>\n<p>La s\u00edntesis de prote\u00ednas no es algo nuevo, pero este nuevo m\u00e9todo permiti\u00f3 obtener prote\u00ednas con la mayor fuerza de interacci\u00f3n jam\u00e1s registrada entre una biomol\u00e9cula dise\u00f1ada por ordenador y su objetivo.<\/p>\n<p>David Baker, catedr\u00e1tico de bioqu\u00edmica de la UW Medicine e investigador del Instituto M\u00e9dico Howard Hughes, fue el autor principal del trabajo de investigaci\u00f3n.<\/p>\n<p>Baker explic\u00f3 la importancia de los resultados diciendo: \"La capacidad de generar nuevas prote\u00ednas con una afinidad y especificidad de uni\u00f3n tan altas abre un mundo de posibilidades, desde nuevos tratamientos de enfermedades hasta diagn\u00f3sticos avanzados\".<\/p>\n<p>Actualmente hay muchas enfermedades que se tratan con anticuerpos. Sin embargo, los anticuerpos son caros de producir y no tienen una gran vida \u00fatil.<\/p>\n<p>Preetham Venkatesh, uno de los investigadores principales, declar\u00f3: \"Hoy en d\u00eda hay muchas enfermedades dif\u00edciles de tratar simplemente porque es todo un reto detectar ciertas mol\u00e9culas en el organismo. Como herramientas para el diagn\u00f3stico, las prote\u00ednas dise\u00f1adas pueden ofrecer una alternativa m\u00e1s rentable que los anticuerpos.\"<\/p>\n<p>Los investigadores pudieron validar sus m\u00e9todos de biodise\u00f1o realizando pruebas de laboratorio en colaboraci\u00f3n con el laboratorio Joseph Rogers de la Universidad de Copenhague y el laboratorio Andrew Hoofnagle de UW Medicine.<\/p>\n<p>Esta investigaci\u00f3n es un magn\u00edfico ejemplo de c\u00f3mo la IA est\u00e1 acelerando el desarrollo de nuevos tratamientos para enfermedades. El art\u00edculo a\u00fan no ha sido revisado por expertos, pero los primeros resultados son muy interesantes.<\/p>","protected":false},"excerpt":{"rendered":"<p>Cient\u00edficos de la Facultad de Medicina de la Universidad de Washington han utilizado la IA para dise\u00f1ar nuevas mol\u00e9culas prote\u00ednicas que abren interesantes posibilidades para la detecci\u00f3n y el tratamiento de enfermedades. La eficacia de un f\u00e1rmaco en el tratamiento de una enfermedad depende en gran medida de su especificidad a la hora de adherirse a la diana causante de la enfermedad y de la fuerza con que se adhiera a ella. La cantidad casi infinita de posibles formas de prote\u00ednas hace que el dise\u00f1o de una prote\u00edna con alta afinidad (fuerza de uni\u00f3n) y alta especificidad sea una tarea extremadamente dif\u00edcil. Encontrar prote\u00ednas que se unan fuertemente a dianas pept\u00eddicas helicoidales como el glucag\u00f3n, el neurop\u00e9ptido Y y el paratiroide<\/p>","protected":false},"author":6,"featured_media":8468,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[150,203,101],"class_list":["post-8436","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-benefits","tag-biotech","tag-medtech"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI-designed proteins display exceptional binding strengths | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/es\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-designed proteins display exceptional binding strengths | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Scientists at the University of Washington School of Medicine used AI to design new protein molecules in a way that opens up exciting possibilities for the detection and treatment of diseases. The effectiveness of a drug in treating a disease depends largely on how specific it is in attaching to the disease-causing target, and how strongly it attaches to the target. The near-infinite amount of potential protein shapes makes designing a protein with both high affinity (binding strength) and high specificity an extremely difficult task. 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