{"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\/pt\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/","title":{"rendered":"Modelo de IA simula 500 milh\u00f5es de anos de evolu\u00e7\u00e3o para criar uma nova prote\u00edna fluorescente"},"content":{"rendered":"

Os cientistas desenvolveram um sistema de IA capaz de simular centenas de milh\u00f5es de anos de evolu\u00e7\u00e3o de prote\u00ednas, criando uma nova prote\u00edna fluorescente diferente de qualquer outra encontrada na natureza.<\/b><\/p>\n

A equipa de investiga\u00e7\u00e3o, liderada por Alexander Rives da EvolutionaryScale, criou um modelo de linguagem de grande dimens\u00e3o (LLM) denominado ESM3 para processar e gerar informa\u00e7\u00f5es sobre sequ\u00eancias, estruturas e fun\u00e7\u00f5es de prote\u00ednas.\u00a0<\/span><\/p>\n

Ao treinar com dados de milhares de milh\u00f5es de prote\u00ednas naturais, o ESM3 aprendeu a prever a forma como as prote\u00ednas podem evoluir e mudar ao longo do tempo.<\/span><\/p>\n

Os investigadores afirmam que o ESM3 n\u00e3o se limita a recuperar ou a recombinar a informa\u00e7\u00e3o existente sobre as prote\u00ednas.\u00a0<\/span><\/p>\n

Em vez disso, parece ter desenvolvido uma compreens\u00e3o dos princ\u00edpios fundamentais que regem a estrutura e a fun\u00e7\u00e3o das prote\u00ednas, o que lhe permite gerar concep\u00e7\u00f5es verdadeiramente inovadoras.<\/span><\/p>\n

\"O ESM3 \u00e9 um simulador emergente que foi aprendido a partir da resolu\u00e7\u00e3o de uma tarefa de previs\u00e3o de tokens em dados gerados pela evolu\u00e7\u00e3o\", explicam os investigadores no <\/span>estudo<\/span><\/a>.<\/span><\/p>\n

\"Foi teorizado que as redes neuronais descobrem a estrutura subjacente dos dados para os quais s\u00e3o treinadas para prever. Desta forma, a resolu\u00e7\u00e3o da tarefa de previs\u00e3o de tokens exigiria que o modelo aprendesse a estrutura profunda que determina os passos que a evolu\u00e7\u00e3o pode dar, ou seja, a biologia fundamental das prote\u00ednas.\"<\/span><\/p>\n

Para testar o modelo, a equipa levou a ESM3 a conceber uma prote\u00edna verde fluorescente (GFP) totalmente nova - um tipo de prote\u00edna respons\u00e1vel pela bioluminesc\u00eancia em certos animais marinhos e muito utilizada na investiga\u00e7\u00e3o biotecnol\u00f3gica.<\/span><\/p>\n

A prote\u00edna gerada por IA, denominada esmGFP, partilha apenas 58% da sua sequ\u00eancia com as prote\u00ednas fluorescentes conhecidas mais semelhantes.<\/span><\/p>\n

O esmGFP apresenta uma luminosidade compar\u00e1vel \u00e0 dos GFPs naturais e mant\u00e9m a estrutura carater\u00edstica em forma de barril, essencial para a fluoresc\u00eancia.\u00a0<\/span><\/p>\n

Os investigadores estimam que a produ\u00e7\u00e3o de uma prote\u00edna t\u00e3o distante das GFPs conhecidas teria levado mais de 500 milh\u00f5es de anos de evolu\u00e7\u00e3o natural.<\/span><\/p>\n

Mais informa\u00e7\u00f5es sobre o estudo<\/h2>\n

O processo de gera\u00e7\u00e3o de esmGFP envolveu v\u00e1rias etapas fundamentais:<\/span><\/p>\n

    \n
  1. Dados<\/strong>: Os investigadores treinaram o ESM3 em cerca de 2,78 mil milh\u00f5es de prote\u00ednas naturais recolhidas de bases de dados de sequ\u00eancias e estruturas. Isto inclui dados da UniRef, MGnify, JGI e outras fontes.<\/li>\n
  2. Arquitetura<\/strong>: O ESM3 utiliza uma arquitetura baseada em transformadores com algumas modifica\u00e7\u00f5es, incluindo um mecanismo de \"aten\u00e7\u00e3o geom\u00e9trica\" para processar estruturas proteicas 3D.<\/li>\n
  3. Prompting<\/b>: Os investigadores forneceram ao ESM3 informa\u00e7\u00f5es estruturais m\u00ednimas a partir de um modelo de GFP (a prote\u00edna fluorescente).<\/span><\/li>\n
  4. Gera\u00e7\u00e3o<\/b>: O ESM3 utilizou este prompt para gerar novas sequ\u00eancias e estruturas de prote\u00ednas atrav\u00e9s de um processo iterativo.<\/span><\/li>\n
  5. Filtragem<\/b>: Milhares de projectos candidatos foram avaliados computacionalmente e filtrados para encontrar os candidatos mais fortes.<\/span><\/li>\n
  6. Ensaios experimentais<\/b>: Os modelos mais promissores foram sintetizados e testados em laborat\u00f3rio quanto \u00e0 atividade fluorescente.<\/span><\/li>\n
  7. Refinamento<\/b>: Depois de identificar uma variante de GFP fraca mas distante, os investigadores utilizaram o ESM3 para otimizar ainda mais o desenho, produzindo finalmente uma prote\u00edna fluorescente mais brilhante.<\/span><\/li>\n<\/ol>\n

    As implica\u00e7\u00f5es desta investiga\u00e7\u00e3o v\u00e3o para al\u00e9m da cria\u00e7\u00e3o de uma \u00fanica prote\u00edna nova.\u00a0<\/span><\/p>\n

    O ESM3 demonstra a capacidade de explorar espa\u00e7os de conce\u00e7\u00e3o de prote\u00ednas muito distantes do que a evolu\u00e7\u00e3o natural produziu, abrindo novas vias para a cria\u00e7\u00e3o de prote\u00ednas com as fun\u00e7\u00f5es ou propriedades desejadas.<\/span><\/p>\n

    Dr. Tiffany Taylor, Professor de Ecologia Microbiana e Evolu\u00e7\u00e3o na Universidade de Bath, que n\u00e3o esteve envolvido no estudo, <\/span>disse ao LiveScience<\/span><\/a>: Neste momento, ainda nos falta a compreens\u00e3o fundamental do modo como as prote\u00ednas, especialmente as \"novas para a ci\u00eancia\", se comportam quando introduzidas num sistema vivo, mas este \u00e9 um novo passo interessante que nos permite abordar a biologia sint\u00e9tica de uma nova forma\".<\/span><\/p>\n

    \"A modela\u00e7\u00e3o por IA como o ESM3 permitir\u00e1 a descoberta de novas prote\u00ednas que os constrangimentos da sele\u00e7\u00e3o natural nunca permitiriam, criando inova\u00e7\u00f5es na engenharia de prote\u00ednas que a evolu\u00e7\u00e3o n\u00e3o consegue\", acrescentou o Dr. Taylor.<\/span><\/p>\n

    Conce\u00e7\u00e3o generativa de prote\u00ednas<\/h2>\n

    A investiga\u00e7\u00e3o e a conce\u00e7\u00e3o de prote\u00ednas com base na IA atingiram um ponto de viragem, com <\/span>DeepMind<\/span><\/span><\/span><\/span>'s AlphaFold 3<\/span><\/a> prevendo como as prote\u00ednas se dobram com uma precis\u00e3o incr\u00edvel.\u00a0<\/span><\/p>\n

    As prote\u00ednas concebidas por IA tamb\u00e9m demonstraram <\/span>excelente resist\u00eancia de liga\u00e7\u00e3o<\/span><\/a>, mostrando que t\u00eam utiliza\u00e7\u00f5es pr\u00e1ticas.\u00a0<\/span><\/p>\n

    No entanto, como acontece com qualquer tecnologia em r\u00e1pida evolu\u00e7\u00e3o que, de alguma forma, interage com a biologia, existem riscos.\u00a0<\/span><\/p>\n

    Em primeiro lugar, se as prote\u00ednas concebidas pela IA se escapassem para o ambiente, poderiam potencialmente interagir com os ecossistemas naturais, chegando mesmo a ultrapassar as prote\u00ednas naturais ou a perturbar os processos biol\u00f3gicos existentes.\u00a0<\/span><\/p>\n

    Em segundo lugar, podem desencadear interac\u00e7\u00f5es inesperadas nos organismos vivos, podendo mesmo criar agentes biol\u00f3gicos nocivos ou toxinas.\u00a0<\/span><\/p>\n

    Os investigadores apelaram recentemente a <\/span>barreiras \u00e9ticas<\/span><\/a> para a conce\u00e7\u00e3o de prote\u00ednas de IA, a fim de evitar resultados arriscados neste dom\u00ednio excitante, embora imprevis\u00edvel.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Os cientistas desenvolveram um sistema de IA capaz de simular centenas de milh\u00f5es de anos de evolu\u00e7\u00e3o de prote\u00ednas, criando uma nova prote\u00edna fluorescente diferente de qualquer outra encontrada na natureza. A equipa de investiga\u00e7\u00e3o, liderada por Alexander Rives da EvolutionaryScale, criou um modelo de linguagem grande (LLM) chamado ESM3 para processar e gerar informa\u00e7\u00f5es sobre sequ\u00eancias, estruturas e fun\u00e7\u00f5es das prote\u00ednas. Ao treinar com dados de milhares de milh\u00f5es de prote\u00ednas naturais, o ESM3 aprendeu a prever a forma como as prote\u00ednas podem evoluir e mudar ao longo do tempo. Os investigadores afirmam que o ESM3 n\u00e3o est\u00e1 simplesmente a recuperar ou a recombinar a informa\u00e7\u00e3o existente sobre as prote\u00ednas. Em vez disso, parece ter desenvolvido uma compreens\u00e3o da<\/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":"\nAI 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\/pt\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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. The researchers argue that ESM3 is not simply retrieving or recombining existing protein information.\u00a0 Instead, it appears to have developed an understanding of the\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/pt\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-07-19T18:14:47+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-20T12:17:50+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/07\/DALL\u00b7E-2024-07-19-19.13.56-A-high-quality-landscape-image-featuring-a-large-glowing-and-intricate-fluorescent-protein-structure-in-a-futuristic-lab.-The-protein-should-have-v.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta 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