{"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\/nb\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/","title":{"rendered":"AI-modell simulerer 500 millioner \u00e5r med evolusjon for \u00e5 skape et nytt fluorescerende protein"},"content":{"rendered":"

Forskere har utviklet et AI-system som kan simulere hundrevis av millioner av \u00e5r med proteinevolusjon og skape et nytt fluorescerende protein som ikke finnes i naturen.<\/b><\/p>\n

Forskerteamet, ledet av Alexander Rives ved EvolutionaryScale, har utviklet en stor spr\u00e5kmodell (LLM) kalt ESM3 for \u00e5 behandle og generere informasjon om proteinsekvenser, -strukturer og -funksjoner.\u00a0<\/span><\/p>\n

Ved \u00e5 trene p\u00e5 data fra milliarder av naturlige proteiner l\u00e6rte ESM3 \u00e5 forutsi hvordan proteiner kan utvikle seg og endre seg over tid.<\/span><\/p>\n

Forskerne hevder at ESM3 ikke bare henter eller rekombinerer eksisterende proteininformasjon.\u00a0<\/span><\/p>\n

I stedet ser det ut til at den har utviklet en forst\u00e5else av de grunnleggende prinsippene som styrer proteiners struktur og funksjon, noe som gj\u00f8r den i stand til \u00e5 generere helt nye design.<\/span><\/p>\n

\"ESM3 er en fremvoksende simulator som har blitt l\u00e6rt fra \u00e5 l\u00f8se en token-prediksjonsoppgave p\u00e5 data generert av evolusjon\", forklarer forskerne i <\/span>studie<\/span><\/a>.<\/span><\/p>\n

\"Det har v\u00e6rt en teori om at nevrale nettverk oppdager den underliggende strukturen i dataene de er trent til \u00e5 forutsi. For \u00e5 l\u00f8se oppgaven med \u00e5 predikere tokener m\u00e5 modellen l\u00e6re seg den dype strukturen som bestemmer hvilke steg evolusjonen kan ta, det vil si den grunnleggende biologien til proteiner.\"<\/span><\/p>\n

For \u00e5 teste modellen fikk teamet ESM3 til \u00e5 designe et helt nytt gr\u00f8nt fluorescerende protein (GFP) - en type protein som er ansvarlig for bioluminescens hos visse marine dyr og mye brukt i bioteknologisk forskning.<\/span><\/p>\n

Det AI-genererte proteinet, kalt esmGFP, deler bare 58% av sekvensen med de mest lignende kjente fluorescerende proteinene.<\/span><\/p>\n

Bemerkelsesverdig nok har esmGFP en lysstyrke som kan sammenlignes med naturlig forekommende GFP-er, og den opprettholder den karakteristiske t\u00f8nneformede strukturen som er avgj\u00f8rende for fluorescens.\u00a0<\/span><\/p>\n

Forskerne ansl\u00e5r at det ville ha tatt over 500 millioner \u00e5r med naturlig evolusjon \u00e5 produsere et protein som er s\u00e5 fjernt fra kjente GFP-er.<\/span><\/p>\n

Mer om studien<\/h2>\n

Prosessen med \u00e5 generere esmGFP involverte flere viktige trinn:<\/span><\/p>\n

    \n
  1. Data<\/strong>: Forskerne trente ESM3 p\u00e5 ca. 2,78 milliarder naturlige proteiner hentet fra sekvens- og strukturdatabaser. Dette inkluderte data fra UniRef, MGnify, JGI og andre kilder.<\/li>\n
  2. Arkitektur<\/strong>: ESM3 bruker en transformatorbasert arkitektur med noen modifikasjoner, inkludert en mekanisme for \"geometrisk oppmerksomhet\" for \u00e5 behandle 3D-proteinstrukturer.<\/li>\n
  3. Prompting<\/b>: Forskerne forsynte ESM3 med minimal strukturell informasjon fra en mal GFP (det fluorescerende proteinet).<\/span><\/li>\n
  4. Generasjon<\/b>: ESM3 brukte denne ledeteksten til \u00e5 generere nye proteinsekvenser og -strukturer gjennom en iterativ prosess.<\/span><\/li>\n
  5. Filtrering<\/b>: Tusenvis av designkandidater ble evaluert og filtrert for \u00e5 finne de sterkeste kandidatene.<\/span><\/li>\n
  6. Eksperimentell testing<\/b>: De mest lovende designene ble syntetisert og testet i laboratoriet for fluorescensaktivitet.<\/span><\/li>\n
  7. Forfining<\/b>: Etter \u00e5 ha identifisert en svak, men fjern GFP-variant, brukte forskerne ESM3 til \u00e5 optimalisere designet ytterligere, og til slutt produserte de et lysere fluorescerende protein.<\/span><\/li>\n<\/ol>\n

    Implikasjonene av denne forskningen strekker seg lenger enn til \u00e5 skape et enkelt nytt protein.\u00a0<\/span><\/p>\n

    ESM3 demonstrerer en evne til \u00e5 utforske proteinutformingsomr\u00e5der som ligger langt unna det den naturlige evolusjonen har frembrakt, noe som \u00e5pner nye muligheter for \u00e5 skape proteiner med \u00f8nskede funksjoner eller egenskaper.<\/span><\/p>\n

    Dr. Tiffany Taylor, professor i mikrobiell \u00f8kologi og evolusjon ved University of Bath, som ikke var involvert i studien, <\/span>fortalte LiveScience<\/span><\/a>: \"Akkurat n\u00e5 mangler vi fortsatt den grunnleggende forst\u00e5elsen av hvordan proteiner, spesielt de som er \"nye for vitenskapen\", oppf\u00f8rer seg n\u00e5r de blir introdusert i et levende system, men dette er et kult nytt skritt som gj\u00f8r at vi kan n\u00e6rme oss syntetisk biologi p\u00e5 en ny m\u00e5te.\"<\/span><\/p>\n

    \"AI-modellering som ESM3 vil gj\u00f8re det mulig \u00e5 oppdage nye proteiner som det naturlige utvalget aldri ville ha tillatt, og dermed skape innovasjoner innen proteinkonstruksjon som evolusjonen ikke kan\", legger Dr. Taylor til.<\/span><\/p>\n

    Generativ proteindesign<\/h2>\n

    AI-drevet proteinforskning og -design har n\u00e5dd et h\u00f8yt niv\u00e5, med <\/span>DeepMind<\/span><\/span><\/span><\/span>'s AlphaFold 3<\/span><\/a> og forutsi hvordan proteiner folder seg med utrolig n\u00f8yaktighet.\u00a0<\/span><\/p>\n

    AI-designede proteiner har ogs\u00e5 vist <\/span>utmerket bindingsstyrke<\/span><\/a>og viser at de har praktiske bruksomr\u00e5der.\u00a0<\/span><\/p>\n

    Men som med all teknologi i rask utvikling som p\u00e5 en eller annen m\u00e5te interagerer med biologi, finnes det risikoer.\u00a0<\/span><\/p>\n

    For det f\u00f8rste kan AI-designede proteiner som slipper ut i milj\u00f8et, potensielt p\u00e5virke naturlige \u00f8kosystemer og til og med utkonkurrere naturlige proteiner eller forstyrre eksisterende biologiske prosesser.\u00a0<\/span><\/p>\n

    For det andre kan de utl\u00f8se uventede interaksjoner i levende organismer, og potensielt skape skadelige biologiske agenser eller giftstoffer.\u00a0<\/span><\/p>\n

    Forskere etterlyste nylig <\/span>etiske kj\u00f8reregler<\/span><\/a> for AI-proteindesign for \u00e5 forhindre risikable utfall p\u00e5 dette spennende, men uforutsigbare feltet.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Forskere har utviklet et AI-system som kan simulere hundrevis av millioner av \u00e5r med proteinevolusjon, og dermed skape et nytt fluorescerende protein som ikke finnes i naturen. Forskerteamet, ledet av Alexander Rives ved EvolutionaryScale, skapte en stor spr\u00e5kmodell (LLM) kalt ESM3 for \u00e5 behandle og generere informasjon om proteinsekvenser, -strukturer og -funksjoner. Ved \u00e5 trene p\u00e5 data fra milliarder av naturlige proteiner l\u00e6rte ESM3 \u00e5 forutsi hvordan proteiner kan utvikle seg og endre seg over tid. Forskerne hevder at ESM3 ikke bare henter eller rekombinerer eksisterende proteininformasjon. I stedet ser det ut til at den har utviklet en forst\u00e5else av<\/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\/nb\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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\/nb\/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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