{"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\/de\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/","title":{"rendered":"KI-Modell simuliert 500 Millionen Jahre Evolution, um ein neues fluoreszierendes Protein zu entwickeln"},"content":{"rendered":"

Wissenschaftler haben ein KI-System entwickelt, das in der Lage ist, Hunderte von Millionen Jahren der Proteinevolution zu simulieren und ein neuartiges fluoreszierendes Protein zu schaffen, das in der Natur nicht vorkommt.<\/b><\/p>\n

Das Forscherteam unter der Leitung von Alexander Rives von EvolutionaryScale hat ein gro\u00dfes Sprachmodell (Large Language Model, LLM) namens ESM3 entwickelt, um Informationen \u00fcber Proteinsequenzen, -strukturen und -funktionen zu verarbeiten und zu generieren.\u00a0<\/span><\/p>\n

Durch Training mit Daten von Milliarden nat\u00fcrlicher Proteine konnte ESM3 vorhersagen, wie sich Proteine im Laufe der Zeit entwickeln und ver\u00e4ndern k\u00f6nnten.<\/span><\/p>\n

Die Forscher argumentieren, dass ESM3 nicht einfach vorhandene Proteininformationen abruft oder neu kombiniert.\u00a0<\/span><\/p>\n

Stattdessen scheint sie ein Verst\u00e4ndnis f\u00fcr die grundlegenden Prinzipien der Proteinstruktur und -funktion entwickelt zu haben, das es ihr erm\u00f6glicht, wirklich neuartige Designs zu entwickeln.<\/span><\/p>\n

\"ESM3 ist ein emergenter Simulator, der durch das L\u00f6sen einer Token-Vorhersageaufgabe auf Daten, die durch die Evolution erzeugt wurden, gelernt wurde\", erkl\u00e4ren die Forscher in der <\/span>Studie<\/span><\/a>.<\/span><\/p>\n

\"Es wurde die Theorie aufgestellt, dass neuronale Netze die zugrunde liegende Struktur der Daten entdecken, f\u00fcr die sie trainiert wurden. Auf diese Weise w\u00fcrde die L\u00f6sung der Token-Vorhersageaufgabe erfordern, dass das Modell die tiefe Struktur lernt, die bestimmt, welche Schritte die Evolution unternehmen kann, d. h. die grundlegende Biologie der Proteine.\"<\/span><\/p>\n

Um das Modell zu testen, veranlasste das Team ESM3, ein v\u00f6llig neues gr\u00fcn fluoreszierendes Protein (GFP) zu entwickeln - eine Art Protein, das f\u00fcr das Biolumineszenzlicht in bestimmten Meerestieren verantwortlich ist und in der biotechnologischen Forschung weit verbreitet ist.<\/span><\/p>\n

Das von der KI erzeugte Protein mit dem Namen esmGFP teilt nur 58% seiner Sequenz mit den \u00e4hnlichsten bekannten fluoreszierenden Proteinen.<\/span><\/p>\n

Bemerkenswert ist, dass esmGFP eine vergleichbare Helligkeit wie nat\u00fcrlich vorkommende GFPs aufweist und die charakteristische tonnenf\u00f6rmige Struktur beibeh\u00e4lt, die f\u00fcr die Fluoreszenz wesentlich ist.\u00a0<\/span><\/p>\n

Die Forscher sch\u00e4tzen, dass die Herstellung eines Proteins, das so weit von den bekannten GFPs entfernt ist, \u00fcber 500 Millionen Jahre nat\u00fcrlicher Evolution erfordert h\u00e4tte.<\/span><\/p>\n

Mehr \u00fcber die Studie<\/h2>\n

Der Prozess zur Erzeugung von esmGFP umfasst mehrere wichtige Schritte:<\/span><\/p>\n

    \n
  1. Daten<\/strong>: Die Forscher trainierten ESM3 mit etwa 2,78 Milliarden nat\u00fcrlichen Proteinen, die in Sequenz- und Strukturdatenbanken gesammelt wurden. Dazu geh\u00f6rten Daten aus UniRef, MGnify, JGI und anderen Quellen.<\/li>\n
  2. Architektur<\/strong>: ESM3 verwendet eine auf Transformern basierende Architektur mit einigen Modifikationen, einschlie\u00dflich eines \"geometrischen Aufmerksamkeits\"-Mechanismus zur Verarbeitung von 3D-Proteinstrukturen.<\/li>\n
  3. Eingabeaufforderung<\/b>: Die Forscher versorgten ESM3 mit minimalen Strukturinformationen aus einer GFP-Vorlage (fluoreszierendes Protein).<\/span><\/li>\n
  4. Generation<\/b>: ESM3 nutzte diese Aufforderung, um in einem iterativen Prozess neue Proteinsequenzen und -strukturen zu erzeugen.<\/span><\/li>\n
  5. Filtern<\/b>: Tausende von Designkandidaten wurden rechnerisch ausgewertet und gefiltert, um die st\u00e4rksten Kandidaten zu finden.<\/span><\/li>\n
  6. Experimentelle Pr\u00fcfung<\/b>: Die vielversprechendsten Designs wurden synthetisiert und im Labor auf ihre Fluoreszenzaktivit\u00e4t getestet.<\/span><\/li>\n
  7. Verfeinerung<\/b>: Nachdem sie eine schwache, aber weit entfernte GFP-Variante identifiziert hatten, nutzten die Forscher ESM3, um das Design weiter zu optimieren und schlie\u00dflich ein helleres fluoreszierendes Protein herzustellen.<\/span><\/li>\n<\/ol>\n

    Die Auswirkungen dieser Forschung gehen \u00fcber die Schaffung eines einzigen neuen Proteins hinaus.\u00a0<\/span><\/p>\n

    ESM3 zeigt, dass es in der Lage ist, R\u00e4ume f\u00fcr das Proteindesign zu erforschen, die weit von dem entfernt sind, was die nat\u00fcrliche Evolution hervorgebracht hat, und er\u00f6ffnet damit neue Wege f\u00fcr die Schaffung von Proteinen mit gew\u00fcnschten Funktionen oder Eigenschaften.<\/span><\/p>\n

    Dr. Tiffany Taylor, Professorin f\u00fcr mikrobielle \u00d6kologie und Evolution an der University of Bath, die nicht an der Studie beteiligt war, <\/span>gegen\u00fcber LiveScience<\/span><\/a>: \"Im Moment fehlt uns noch das grundlegende Verst\u00e4ndnis daf\u00fcr, wie sich Proteine, insbesondere solche, die 'neu in der Wissenschaft' sind, verhalten, wenn sie in ein lebendes System eingebracht werden, aber dies ist ein cooler neuer Schritt, der es uns erm\u00f6glicht, die synthetische Biologie auf eine neue Art und Weise anzugehen.\"<\/span><\/p>\n

    \"KI-Modelle wie ESM3 werden die Entdeckung neuer Proteine erm\u00f6glichen, die die nat\u00fcrliche Selektion niemals zulassen w\u00fcrde, und damit Innovationen im Protein-Engineering schaffen, die die Evolution nicht zul\u00e4sst\", f\u00fcgte Dr. Taylor hinzu.<\/span><\/p>\n

    Generatives Proteindesign<\/h2>\n

    Die KI-gesteuerte Proteinforschung und -entwicklung hat ein hohes Niveau erreicht, mit <\/span>DeepMind<\/span><\/span><\/span><\/span>AlphaFold 3<\/span><\/a> mit unglaublicher Genauigkeit vorhersagen, wie sich Proteine falten.\u00a0<\/span><\/p>\n

    Von der k\u00fcnstlichen Intelligenz entworfene Proteine haben auch gezeigt <\/span>ausgezeichnete Bindungsst\u00e4rke<\/span><\/a>und zeigen damit, dass sie einen praktischen Nutzen haben.\u00a0<\/span><\/p>\n

    Wie bei jeder sich schnell entwickelnden Technologie, die in irgendeiner Weise mit der Biologie interagiert, gibt es jedoch auch Risiken.\u00a0<\/span><\/p>\n

    Erstens k\u00f6nnten von der KI entwickelte Proteine, wenn sie in die Umwelt entweichen, mit nat\u00fcrlichen \u00d6kosystemen interagieren und sogar nat\u00fcrliche Proteine verdr\u00e4ngen oder bestehende biologische Prozesse st\u00f6ren.\u00a0<\/span><\/p>\n

    Zweitens k\u00f6nnten sie unerwartete Wechselwirkungen in lebenden Organismen ausl\u00f6sen und m\u00f6glicherweise sogar sch\u00e4dliche biologische Wirkstoffe oder Toxine erzeugen.\u00a0<\/span><\/p>\n

    Forscher forderten k\u00fcrzlich <\/span>ethische Leitplanken<\/span><\/a> f\u00fcr das Design von KI-Proteinen, um riskante Ergebnisse in diesem spannenden, wenn auch unberechenbaren Bereich zu verhindern.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Wissenschaftler haben ein KI-System entwickelt, das in der Lage ist, Hunderte von Millionen Jahren der Proteinevolution zu simulieren und ein neuartiges fluoreszierendes Protein zu erzeugen, das in der Natur nicht vorkommt. Das Forscherteam unter der Leitung von Alexander Rives von EvolutionaryScale hat ein gro\u00dfes Sprachmodell (Large Language Model, LLM) namens ESM3 entwickelt, das Informationen \u00fcber Proteinsequenzen, -strukturen und -funktionen verarbeitet und generiert. Durch Training mit Daten von Milliarden nat\u00fcrlicher Proteine lernte ESM3, vorherzusagen, wie sich Proteine im Laufe der Zeit entwickeln und ver\u00e4ndern k\u00f6nnten. Die Forscher argumentieren, dass ESM3 nicht einfach vorhandene Proteininformationen abruft oder neu kombiniert. Vielmehr scheint es ein Verst\u00e4ndnis f\u00fcr die<\/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\/de\/2024\/07\/ai-model-simulates-500-million-years-of-evolution-to-create-a-novel-fluorescent-protein\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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\/de\/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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