{"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\/da\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/","title":{"rendered":"AI-model simulerer 500 millioner \u00e5rs evolution for at skabe et nyt fluorescerende protein"},"content":{"rendered":"

Forskere har udviklet et AI-system, der er i stand til at simulere hundredvis af millioner \u00e5rs proteinevolution og skabe et nyt fluorescerende protein, som ikke findes i naturen.<\/b><\/p>\n

Forskerteamet, ledet af Alexander Rives fra EvolutionaryScale, skabte en stor sprogmodel (LLM) kaldet ESM3 til at behandle og generere information om proteinsekvenser, -strukturer og -funktioner.\u00a0<\/span><\/p>\n

Ved at tr\u00e6ne p\u00e5 data fra milliarder af naturlige proteiner l\u00e6rte ESM3 at forudsige, hvordan proteiner kunne udvikle sig og \u00e6ndre sig over tid.<\/span><\/p>\n

Forskerne h\u00e6vder, at ESM3 ikke bare henter eller rekombinerer eksisterende proteininformation.\u00a0<\/span><\/p>\n

I stedet ser det ud til, at den har udviklet en forst\u00e5else af de grundl\u00e6ggende principper for proteinstruktur og -funktion, som g\u00f8r den i stand til at skabe helt nye designs.<\/span><\/p>\n

\"ESM3 er en emergent simulator, der er blevet l\u00e6rt ved at l\u00f8se en token-forudsigelsesopgave p\u00e5 data genereret af evolutionen\", forklarer forskerne i rapporten. <\/span>unders\u00f8gelse<\/span><\/a>.<\/span><\/p>\n

\"Det er blevet teoretiseret, at neurale netv\u00e6rk opdager den underliggende struktur i de data, de er tr\u00e6net til at forudsige. P\u00e5 den m\u00e5de vil l\u00f8sningen af opgaven med at forudsige tokener kr\u00e6ve, at modellen l\u00e6rer den dybe struktur, der bestemmer, hvilke skridt evolutionen kan tage, dvs. proteinernes grundl\u00e6ggende biologi.\"<\/span><\/p>\n

For at teste modellen fik teamet ESM3 til at designe et helt nyt gr\u00f8nt fluorescerende protein (GFP) - en type protein, der er ansvarlig for bioluminescens hos visse havdyr, og som er meget udbredt inden for bioteknologisk forskning.<\/span><\/p>\n

Det AI-genererede protein, kaldet esmGFP, deler kun 58% af sin sekvens med de mest lignende kendte fluorescerende proteiner.<\/span><\/p>\n

Bem\u00e6rkelsesv\u00e6rdigt nok udviser esmGFP en lysstyrke, der kan sammenlignes med naturligt forekommende GFP'er, og bevarer den karakteristiske t\u00f8ndeformede struktur, der er afg\u00f8rende for fluorescens.\u00a0<\/span><\/p>\n

Forskerne vurderer, at det ville have taget over 500 millioner \u00e5r med naturlig evolution at producere et protein, der ligger s\u00e5 langt fra kendte GFP'er.<\/span><\/p>\n

Mere om unders\u00f8gelsen<\/h2>\n

Processen med at generere esmGFP involverede flere vigtige trin:<\/span><\/p>\n

    \n
  1. Data<\/strong>: Forskerne tr\u00e6nede ESM3 p\u00e5 ca. 2,78 milliarder naturlige proteiner indsamlet fra sekvens- og strukturdatabaser. Dette omfattede data fra UniRef, MGnify, JGI og andre kilder.<\/li>\n
  2. Arkitektur<\/strong>: ESM3 bruger en transformer-baseret arkitektur med nogle modifikationer, herunder en \"geometrisk opm\u00e6rksomhed\"-mekanisme til at behandle 3D-proteinstrukturer.<\/li>\n
  3. Prompting<\/b>: Forskerne forsynede ESM3 med minimal strukturel information fra en skabelon GFP (det fluorescerende protein).<\/span><\/li>\n
  4. Generation<\/b>: ESM3 brugte denne prompt til at generere nye proteinsekvenser og -strukturer gennem en iterativ proces.<\/span><\/li>\n
  5. Filtrering<\/b>: Tusindvis af kandidatdesigns blev computerevalueret og filtreret for at finde de st\u00e6rkeste kandidater.<\/span><\/li>\n
  6. Eksperimentel afpr\u00f8vning<\/b>: De mest lovende designs blev syntetiseret og testet i laboratoriet for fluorescensaktivitet.<\/span><\/li>\n
  7. For\u00e6dling<\/b>: Efter at have identificeret en svag, men fjern GFP-variant, brugte forskerne ESM3 til yderligere at optimere designet og producerede i sidste ende et lysere fluorescerende protein.<\/span><\/li>\n<\/ol>\n

    Konsekvenserne af denne forskning r\u00e6kker ud over skabelsen af et enkelt nyt protein.\u00a0<\/span><\/p>\n

    ESM3 demonstrerer en evne til at udforske proteindesignomr\u00e5der, der ligger langt fra, hvad den naturlige evolution har frembragt, hvilket \u00e5bner nye muligheder for at skabe proteiner med \u00f8nskede funktioner eller egenskaber.<\/span><\/p>\n

    Dr. Tiffany Taylor, professor i mikrobiel \u00f8kologi og evolution ved University of Bath, som ikke var involveret i unders\u00f8gelsen, <\/span>fortalte LiveScience<\/span><\/a>: \"Lige nu mangler vi stadig den grundl\u00e6ggende forst\u00e5else af, hvordan proteiner, is\u00e6r dem, der er \"nye for videnskaben\", opf\u00f8rer sig, n\u00e5r de introduceres i et levende system, men dette er et sejt nyt skridt, der giver os mulighed for at n\u00e6rme os syntetisk biologi p\u00e5 en ny m\u00e5de.\"<\/span><\/p>\n

    \"AI-modellering som ESM3 vil g\u00f8re det muligt at opdage nye proteiner, som den naturlige udv\u00e6lgelse aldrig ville tillade, og skabe innovationer inden for proteinteknologi, som evolutionen ikke kan\", tilf\u00f8jer Dr. Taylor.<\/span><\/p>\n

    Generativt proteindesign<\/h2>\n

    AI-drevet proteinforskning og -design har n\u00e5et et h\u00f8jdepunkt med <\/span>DeepMind<\/span><\/span><\/span><\/span>'s AlphaFold 3<\/span><\/a> at forudsige, hvordan proteiner folder sig med utrolig n\u00f8jagtighed.\u00a0<\/span><\/p>\n

    AI-designede proteiner har ogs\u00e5 vist <\/span>Fremragende bindingsstyrke<\/span><\/a>og viser, at de har praktiske anvendelser.\u00a0<\/span><\/p>\n

    Men som med enhver teknologi i rivende udvikling, der p\u00e5 en eller anden m\u00e5de interagerer med biologi, er der risici.\u00a0<\/span><\/p>\n

    For det f\u00f8rste: Hvis AI-designede proteiner slipper ud i milj\u00f8et, kan de potentielt interagere med naturlige \u00f8kosystemer og endda udkonkurrere naturlige proteiner eller forstyrre eksisterende biologiske processer.\u00a0<\/span><\/p>\n

    For det andet kan de udl\u00f8se uventede interaktioner i levende organismer og potentielt endda skabe skadelige biologiske stoffer eller toksiner.\u00a0<\/span><\/p>\n

    Forskere opfordrede for nylig til <\/span>Etiske v\u00e6rn<\/span><\/a> for AI-proteindesign for at forhindre risikable udfald p\u00e5 dette sp\u00e6ndende, men uforudsigelige omr\u00e5de.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskere har udviklet et AI-system, der er i stand til at simulere hundredvis af millioner \u00e5rs proteinevolution og skabe et nyt fluorescerende protein, der ikke ligner noget, der findes i naturen. Forskergruppen, der ledes af Alexander Rives fra EvolutionaryScale, skabte en stor sprogmodel (LLM) kaldet ESM3 til at behandle og generere information om proteinsekvenser, strukturer og funktioner. Ved at tr\u00e6ne p\u00e5 data fra milliarder af naturlige proteiner l\u00e6rte ESM3 at forudsige, hvordan proteiner kan udvikle sig og \u00e6ndre sig over tid. Forskerne h\u00e6vder, at ESM3 ikke blot henter eller rekombinerer eksisterende proteininformation. I stedet ser det ud til, at den har udviklet en forst\u00e5else af<\/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\/da\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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\/da\/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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