{"id":13507,"date":"2024-07-19T18:14:47","date_gmt":"2024-07-19T18:14:47","guid":{"rendered":"https:\/\/dailyai.com\/?p=13507"},"modified":"2024-07-20T12:17:50","modified_gmt":"2024-07-20T12:17:50","slug":"ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/","title":{"rendered":"Un modelo de IA simula 500 millones de a\u00f1os de evoluci\u00f3n para crear una nueva prote\u00edna fluorescente"},"content":{"rendered":"<p><b>Los cient\u00edficos han desarrollado un sistema de IA capaz de simular cientos de millones de a\u00f1os de evoluci\u00f3n de las prote\u00ednas, creando una nueva prote\u00edna fluorescente distinta de las que se encuentran en la naturaleza.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">El equipo de investigaci\u00f3n, dirigido por Alexander Rives en EvolutionaryScale, cre\u00f3 un gran modelo de lenguaje (LLM) llamado ESM3 para procesar y generar informaci\u00f3n sobre secuencias, estructuras y funciones de prote\u00ednas.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Al entrenarse con datos de miles de millones de prote\u00ednas naturales, ESM3 aprendi\u00f3 a predecir c\u00f3mo podr\u00edan evolucionar y cambiar las prote\u00ednas con el tiempo.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Los investigadores sostienen que ESM3 no se limita a recuperar o recombinar informaci\u00f3n existente sobre prote\u00ednas.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">En cambio, parece haber desarrollado una comprensi\u00f3n de los principios fundamentales que rigen la estructura y la funci\u00f3n de las prote\u00ednas, lo que le permite generar dise\u00f1os realmente novedosos.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\"ESM3 es un simulador emergente que se ha aprendido a partir de la resoluci\u00f3n de una tarea de predicci\u00f3n de fichas sobre datos generados por la evoluci\u00f3n\", explican los investigadores en el <\/span><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2024.07.01.600583v1.full.pdf\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">estudiar<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\"Se ha teorizado que las redes neuronales descubren la estructura subyacente de los datos que se les entrena para predecir. De este modo, resolver la tarea de predicci\u00f3n de tokens requerir\u00eda que el modelo aprendiera la estructura profunda que determina qu\u00e9 pasos puede dar la evoluci\u00f3n, es decir, la biolog\u00eda fundamental de las prote\u00ednas.\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Para probar el modelo, el equipo recurri\u00f3 al ESM3 para dise\u00f1ar una prote\u00edna verde fluorescente (GFP) totalmente nueva, un tipo de prote\u00edna responsable de la bioluminiscencia en ciertos animales marinos y muy utilizada en la investigaci\u00f3n biotecnol\u00f3gica.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La prote\u00edna generada por la IA, denominada esmGFP, s\u00f3lo comparte 58% de su secuencia con las prote\u00ednas fluorescentes conocidas m\u00e1s similares.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sorprendentemente, la esmGFP exhibe un brillo comparable al de las GFP naturales y mantiene la estructura caracter\u00edstica en forma de barril, esencial para la fluorescencia.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Los investigadores calculan que producir una prote\u00edna tan distante de las GFP conocidas habr\u00eda llevado m\u00e1s de 500 millones de a\u00f1os de evoluci\u00f3n natural.<\/span><\/p>\n<h2>M\u00e1s informaci\u00f3n sobre el estudio<\/h2>\n<p><span style=\"font-weight: 400;\">El proceso de generaci\u00f3n de esmGFP implic\u00f3 varios pasos clave:<\/span><\/p>\n<ol>\n<li class=\"whitespace-normal break-words\"><strong>Datos<\/strong>: Los investigadores entrenaron ESM3 con aproximadamente 2.780 millones de prote\u00ednas naturales recogidas de bases de datos de secuencias y estructuras. Esto incluye datos de UniRef, MGnify, JGI y otras fuentes.<\/li>\n<li class=\"whitespace-normal break-words\"><strong>Arquitectura<\/strong>: ESM3 utiliza una arquitectura basada en transformadores con algunas modificaciones, incluido un mecanismo de \"atenci\u00f3n geom\u00e9trica\" para procesar estructuras de prote\u00ednas en 3D.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Para consultar<\/b><span style=\"font-weight: 400;\">: Los investigadores proporcionaron a ESM3 informaci\u00f3n estructural m\u00ednima a partir de una plantilla GFP (la prote\u00edna fluorescente).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Generaci\u00f3n<\/b><span style=\"font-weight: 400;\">: ESM3 utiliz\u00f3 esta indicaci\u00f3n para generar nuevas secuencias y estructuras de prote\u00ednas mediante un proceso iterativo.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Filtrado<\/b><span style=\"font-weight: 400;\">: Miles de dise\u00f1os candidatos fueron evaluados computacionalmente y filtrados para encontrar los candidatos m\u00e1s fuertes.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Pruebas experimentales<\/b><span style=\"font-weight: 400;\">: Los dise\u00f1os m\u00e1s prometedores se sintetizaron y probaron en el laboratorio para determinar su actividad fluorescente.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Perfeccionamiento<\/b><span style=\"font-weight: 400;\">: Tras identificar una variante de GFP tenue pero distante, los investigadores utilizaron ESM3 para optimizar a\u00fan m\u00e1s el dise\u00f1o, produciendo finalmente una prote\u00edna fluorescente m\u00e1s brillante.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Las implicaciones de esta investigaci\u00f3n van m\u00e1s all\u00e1 de la creaci\u00f3n de una \u00fanica prote\u00edna novedosa.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">ESM3 demuestra su capacidad para explorar espacios de dise\u00f1o de prote\u00ednas muy alejados de los que ha producido la evoluci\u00f3n natural, lo que abre nuevas v\u00edas para crear prote\u00ednas con las funciones o propiedades deseadas.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">La Dra. Tiffany Taylor, catedr\u00e1tica de Ecolog\u00eda y Evoluci\u00f3n Microbiana de la Universidad de Bath, que no particip\u00f3 en el estudio, <\/span><a href=\"https:\/\/www.livescience.com\/technology\/artificial-intelligence\/chatgpt-moment-for-biology-ex-meta-scientists-develop-ai-that-creates-proteins-not-found-in-nature\"><span style=\"font-weight: 400;\">a LiveScience<\/span><\/a><span style=\"font-weight: 400;\">: \"Ahora mismo, a\u00fan nos falta la comprensi\u00f3n fundamental de c\u00f3mo se comportan las prote\u00ednas, especialmente las 'nuevas para la ciencia', cuando se introducen en un sistema vivo, pero este es un nuevo paso genial que nos permite abordar la biolog\u00eda sint\u00e9tica de una forma nueva.\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\"Los modelos de IA como el ESM3 permitir\u00e1n descubrir nuevas prote\u00ednas que las limitaciones de la selecci\u00f3n natural nunca permitir\u00edan, creando innovaciones en ingenier\u00eda de prote\u00ednas que la evoluci\u00f3n no puede\", a\u00f1adi\u00f3 el Dr. Taylor.<\/span><\/p>\n<h2>Dise\u00f1o generativo de prote\u00ednas<\/h2>\n<p><span style=\"font-weight: 400;\">La investigaci\u00f3n y el dise\u00f1o de prote\u00ednas impulsados por la IA han alcanzado su punto \u00e1lgido, con <\/span><a href=\"https:\/\/dailyai.com\/es\/2024\/05\/alphafold-3-deepmind-evolves-its-ai-protein-folding-project\/\"><span style=\"font-weight: 400;\"><span class=\"noTranslate\" data-no-translation=\"\"><span class=\"noTranslate\" data-no-translation=\"\"><span class=\"noTranslate\" data-no-translation=\"\"><span class=\"noTranslate\" data-no-translation=\"\">DeepMind<\/span><\/span><\/span><\/span>AlphaFold 3<\/span><\/a><span style=\"font-weight: 400;\"> predecir c\u00f3mo se pliegan las prote\u00ednas con una precisi\u00f3n incre\u00edble.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Las prote\u00ednas dise\u00f1adas con IA tambi\u00e9n han demostrado <\/span><a href=\"https:\/\/dailyai.com\/es\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/\"><span style=\"font-weight: 400;\">excelente resistencia de encuadernaci\u00f3n<\/span><\/a><span style=\"font-weight: 400;\">demostrando que tienen usos pr\u00e1cticos.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sin embargo, como ocurre con cualquier tecnolog\u00eda de r\u00e1pida evoluci\u00f3n que interact\u00faa de alg\u00fan modo con la biolog\u00eda, existen riesgos.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">En primer lugar, si las prote\u00ednas dise\u00f1adas por la IA se escaparan al medio ambiente, podr\u00edan interactuar con los ecosistemas naturales, incluso superando a las prote\u00ednas naturales o perturbando los procesos biol\u00f3gicos existentes.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">En segundo lugar, podr\u00edan desencadenar interacciones inesperadas dentro de los organismos vivos, pudiendo incluso crear agentes biol\u00f3gicos nocivos o toxinas.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Los investigadores han pedido recientemente <\/span><a href=\"https:\/\/dailyai.com\/es\/2024\/03\/researchers-create-voluntary-safety-rules-for-ai-protein-design\/\"><span style=\"font-weight: 400;\">barreras \u00e9ticas<\/span><\/a><span style=\"font-weight: 400;\"> para que el dise\u00f1o de prote\u00ednas de IA evite resultados arriesgados en este apasionante, aunque impredecible, campo.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Los cient\u00edficos han desarrollado un sistema de IA capaz de simular cientos de millones de a\u00f1os de evoluci\u00f3n de las prote\u00ednas, creando una nueva prote\u00edna fluorescente distinta de las que se encuentran en la naturaleza. El equipo de investigaci\u00f3n, dirigido por Alexander Rives, de EvolutionaryScale, cre\u00f3 un modelo de gran lenguaje (LLM) llamado ESM3 para procesar y generar informaci\u00f3n sobre secuencias, estructuras y funciones de prote\u00ednas.  Al entrenarse con datos de miles de millones de prote\u00ednas naturales, ESM3 aprendi\u00f3 a predecir c\u00f3mo podr\u00edan evolucionar y cambiar las prote\u00ednas con el tiempo. Los investigadores sostienen que ESM3 no se limita a recuperar o recombinar la informaci\u00f3n existente sobre prote\u00ednas.  En cambio, parece haber desarrollado una comprensi\u00f3n de la<\/p>","protected":false},"author":2,"featured_media":13508,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[353,105,178],"class_list":["post-13507","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-biology","tag-machine-learning","tag-medicine"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI model simulates 500 million years of evolution to create a novel fluorescent protein | 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\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI model simulates 500 million years of evolution to create a novel fluorescent protein | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Scientists have developed an AI system capable of simulating hundreds of millions of years of protein evolution, creating a novel fluorescent protein unlike any found in nature. The research team, led by Alexander Rives at EvolutionaryScale, created a large language model (LLM) called ESM3 to process and generate information about protein sequences, structures, and functions.\u00a0 By training on data from billions of natural proteins, ESM3 learned to predict how proteins might evolve and change over time. 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The research team, led by Alexander Rives at EvolutionaryScale, created a large language model (LLM) called ESM3 to process and generate information about protein sequences, structures, and functions.\u00a0 By training on data from billions of natural proteins, ESM3 learned to predict how proteins might evolve and change over time. 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