{"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\/fr\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/","title":{"rendered":"Un mod\u00e8le d'IA simule 500 millions d'ann\u00e9es d'\u00e9volution pour cr\u00e9er une nouvelle prot\u00e9ine fluorescente"},"content":{"rendered":"

Des scientifiques ont mis au point un syst\u00e8me d'intelligence artificielle capable de simuler des centaines de millions d'ann\u00e9es d'\u00e9volution des prot\u00e9ines, cr\u00e9ant ainsi une nouvelle prot\u00e9ine fluorescente qui ne ressemble \u00e0 aucune autre dans la nature.<\/b><\/p>\n

L'\u00e9quipe de recherche, dirig\u00e9e par Alexander Rives \u00e0 EvolutionaryScale, a cr\u00e9\u00e9 un grand mod\u00e8le de langage (LLM) appel\u00e9 ESM3 pour traiter et g\u00e9n\u00e9rer des informations sur les s\u00e9quences, les structures et les fonctions des prot\u00e9ines.\u00a0<\/span><\/p>\n

En s'entra\u00eenant sur des donn\u00e9es provenant de milliards de prot\u00e9ines naturelles, ESM3 a appris \u00e0 pr\u00e9dire comment les prot\u00e9ines pouvaient \u00e9voluer et se modifier au fil du temps.<\/span><\/p>\n

Les chercheurs affirment que l'ESM3 ne se contente pas de r\u00e9cup\u00e9rer ou de recombiner les informations existantes sur les prot\u00e9ines.\u00a0<\/span><\/p>\n

Au contraire, elle semble avoir d\u00e9velopp\u00e9 une compr\u00e9hension des principes fondamentaux r\u00e9gissant la structure et la fonction des prot\u00e9ines, ce qui lui permet de g\u00e9n\u00e9rer des conceptions v\u00e9ritablement nouvelles.<\/span><\/p>\n

\"ESM3 est un simulateur \u00e9mergent qui a \u00e9t\u00e9 appris en r\u00e9solvant une t\u00e2che de pr\u00e9diction de jetons sur des donn\u00e9es g\u00e9n\u00e9r\u00e9es par l'\u00e9volution\", expliquent les chercheurs dans le document <\/span>\u00e9tude<\/span><\/a>.<\/span><\/p>\n

\"La th\u00e9orie veut que les r\u00e9seaux neuronaux d\u00e9couvrent la structure sous-jacente des donn\u00e9es qu'ils sont entra\u00een\u00e9s \u00e0 pr\u00e9dire. Ainsi, pour r\u00e9soudre la t\u00e2che de pr\u00e9diction des jetons, il faudrait que le mod\u00e8le apprenne la structure profonde qui d\u00e9termine les \u00e9tapes de l'\u00e9volution, c'est-\u00e0-dire la biologie fondamentale des prot\u00e9ines.\"<\/span><\/p>\n

Pour tester le mod\u00e8le, l'\u00e9quipe a demand\u00e9 \u00e0 ESM3 de concevoir une toute nouvelle prot\u00e9ine fluorescente verte (GFP) - un type de prot\u00e9ine responsable de la bioluminescence chez certains animaux marins et largement utilis\u00e9e dans la recherche biotechnologique.<\/span><\/p>\n

La prot\u00e9ine g\u00e9n\u00e9r\u00e9e par l'IA, appel\u00e9e esmGFP, ne partage que 58% de sa s\u00e9quence avec les prot\u00e9ines fluorescentes connues les plus similaires.<\/span><\/p>\n

Il est remarquable que l'esmGFP pr\u00e9sente une luminosit\u00e9 comparable \u00e0 celle des GFP naturelles et qu'elle conserve la structure caract\u00e9ristique en forme de tonneau, essentielle \u00e0 la fluorescence.\u00a0<\/span><\/p>\n

Les chercheurs estiment que la production d'une prot\u00e9ine aussi \u00e9loign\u00e9e des GFP connues aurait n\u00e9cessit\u00e9 plus de 500 millions d'ann\u00e9es d'\u00e9volution naturelle.<\/span><\/p>\n

En savoir plus sur l'\u00e9tude<\/h2>\n

Le processus de g\u00e9n\u00e9ration de l'esmGFP comporte plusieurs \u00e9tapes cl\u00e9s :<\/span><\/p>\n

    \n
  1. Donn\u00e9es<\/strong>: Les chercheurs ont entra\u00een\u00e9 ESM3 sur environ 2,78 milliards de prot\u00e9ines naturelles provenant de bases de donn\u00e9es de s\u00e9quences et de structures. Il s'agit de donn\u00e9es provenant d'UniRef, MGnify, JGI et d'autres sources.<\/li>\n
  2. L'architecture<\/strong>: ESM3 utilise une architecture bas\u00e9e sur les transformateurs avec quelques modifications, y compris un m\u00e9canisme d'\"attention g\u00e9om\u00e9trique\" pour traiter les structures de prot\u00e9ines en 3D.<\/li>\n
  3. Prompt \u00e0 l'action<\/b>: Les chercheurs ont fourni \u00e0 ESM3 des informations structurelles minimales \u00e0 partir d'un mod\u00e8le de GFP (prot\u00e9ine fluorescente).<\/span><\/li>\n
  4. G\u00e9n\u00e9ration<\/b>: ESM3 a utilis\u00e9 cette invite pour g\u00e9n\u00e9rer de nouvelles s\u00e9quences et structures de prot\u00e9ines par le biais d'un processus it\u00e9ratif.<\/span><\/li>\n
  5. Filtrage<\/b>: Des milliers de mod\u00e8les candidats ont \u00e9t\u00e9 \u00e9valu\u00e9s par calcul et filtr\u00e9s pour trouver les candidats les plus forts.<\/span><\/li>\n
  6. Essais exp\u00e9rimentaux<\/b>: Les mod\u00e8les les plus prometteurs ont \u00e9t\u00e9 synth\u00e9tis\u00e9s et test\u00e9s en laboratoire pour l'activit\u00e9 de fluorescence.<\/span><\/li>\n
  7. Raffinement<\/b>: Apr\u00e8s avoir identifi\u00e9 une variante de la GFP peu lumineuse mais \u00e9loign\u00e9e, les chercheurs ont utilis\u00e9 l'ESM3 pour optimiser davantage la conception, produisant finalement une prot\u00e9ine fluorescente plus lumineuse.<\/span><\/li>\n<\/ol>\n

    Les implications de cette recherche vont au-del\u00e0 de la cr\u00e9ation d'une seule nouvelle prot\u00e9ine.\u00a0<\/span><\/p>\n

    ESM3 d\u00e9montre une capacit\u00e9 \u00e0 explorer des espaces de conception de prot\u00e9ines tr\u00e8s \u00e9loign\u00e9s de ce que l'\u00e9volution naturelle a produit, ouvrant de nouvelles voies pour cr\u00e9er des prot\u00e9ines avec des fonctions ou des propri\u00e9t\u00e9s souhait\u00e9es.<\/span><\/p>\n

    Tiffany Taylor, professeur d'\u00e9cologie microbienne et d'\u00e9volution \u00e0 l'universit\u00e9 de Bath, qui n'a pas particip\u00e9 \u00e0 l'\u00e9tude, <\/span>a d\u00e9clar\u00e9 \u00e0 LiveScience<\/span><\/a>: \"\u00c0 l'heure actuelle, nous manquons encore de connaissances fondamentales sur la mani\u00e8re dont les prot\u00e9ines, en particulier celles qui sont \"nouvelles pour la science\", se comportent lorsqu'elles sont introduites dans un syst\u00e8me vivant, mais il s'agit l\u00e0 d'une nouvelle \u00e9tape int\u00e9ressante qui nous permet d'aborder la biologie synth\u00e9tique d'une nouvelle mani\u00e8re.\"<\/span><\/p>\n

    \"La mod\u00e9lisation de l'IA comme ESM3 permettra de d\u00e9couvrir de nouvelles prot\u00e9ines que les contraintes de la s\u00e9lection naturelle ne permettraient jamais, cr\u00e9ant ainsi des innovations dans l'ing\u00e9nierie des prot\u00e9ines que l'\u00e9volution ne peut pas r\u00e9aliser\", a ajout\u00e9 le Dr Taylor.<\/span><\/p>\n

    Conception g\u00e9n\u00e9rative de prot\u00e9ines<\/h2>\n

    La recherche et la conception de prot\u00e9ines bas\u00e9es sur l'IA sont en plein essor. <\/span>DeepMind<\/span><\/span><\/span><\/span>AlphaFold 3<\/span><\/a> pr\u00e9dire la fa\u00e7on dont les prot\u00e9ines se replient avec une pr\u00e9cision incroyable.\u00a0<\/span><\/p>\n

    Les prot\u00e9ines con\u00e7ues par l'IA ont \u00e9galement montr\u00e9 <\/span>excellente force de liaison<\/span><\/a>Les produits de l'agriculture et de l'\u00e9levage sont des produits de consommation courante, ce qui montre qu'ils ont des utilisations pratiques.\u00a0<\/span><\/p>\n

    Cependant, comme pour toute technologie en \u00e9volution rapide qui interagit d'une mani\u00e8re ou d'une autre avec la biologie, il existe des risques.\u00a0<\/span><\/p>\n

    Premi\u00e8rement, si des prot\u00e9ines con\u00e7ues par l'IA s'\u00e9chappaient dans l'environnement, elles pourraient potentiellement interagir avec les \u00e9cosyst\u00e8mes naturels, voire m\u00eame surpasser les prot\u00e9ines naturelles ou perturber les processus biologiques existants.\u00a0<\/span><\/p>\n

    Deuxi\u00e8mement, ils pourraient d\u00e9clencher des interactions inattendues au sein des organismes vivants, voire cr\u00e9er des agents biologiques nocifs ou des toxines.\u00a0<\/span><\/p>\n

    Les chercheurs ont r\u00e9cemment appel\u00e9 \u00e0 <\/span>garde-fous \u00e9thiques<\/span><\/a> pour la conception de l'IA et des prot\u00e9ines afin d'\u00e9viter les r\u00e9sultats risqu\u00e9s dans ce domaine passionnant, bien qu'impr\u00e9visible.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Des scientifiques ont mis au point un syst\u00e8me d'intelligence artificielle capable de simuler des centaines de millions d'ann\u00e9es d'\u00e9volution des prot\u00e9ines, cr\u00e9ant ainsi une nouvelle prot\u00e9ine fluorescente qui ne ressemble \u00e0 aucune autre dans la nature. L'\u00e9quipe de recherche, dirig\u00e9e par Alexander Rives \u00e0 EvolutionaryScale, a cr\u00e9\u00e9 un grand mod\u00e8le de langage (LLM) appel\u00e9 ESM3 pour traiter et g\u00e9n\u00e9rer des informations sur les s\u00e9quences, les structures et les fonctions des prot\u00e9ines. En s'entra\u00eenant sur des donn\u00e9es provenant de milliards de prot\u00e9ines naturelles, ESM3 a appris \u00e0 pr\u00e9dire comment les prot\u00e9ines pourraient \u00e9voluer et se modifier au fil du temps. Les chercheurs affirment que l'ESM3 ne se contente pas de r\u00e9cup\u00e9rer ou de recombiner des informations existantes sur les prot\u00e9ines. Au contraire, il semble avoir d\u00e9velopp\u00e9 une compr\u00e9hension de l'\u00e9volution des prot\u00e9ines.<\/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\/fr\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\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\/fr\/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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