{"id":8436,"date":"2023-12-19T16:35:20","date_gmt":"2023-12-19T16:35:20","guid":{"rendered":"https:\/\/dailyai.com\/?p=8436"},"modified":"2023-12-19T16:35:20","modified_gmt":"2023-12-19T16:35:20","slug":"ai-designed-proteins-display-exceptional-binding-strengths","status":"publish","type":"post","link":"https:\/\/dailyai.com\/de\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/","title":{"rendered":"KI-entwickelte Proteine zeigen au\u00dfergew\u00f6hnliche Bindungsst\u00e4rke"},"content":{"rendered":"<p><strong>Wissenschaftler der University of Washington School of Medicine haben mithilfe von KI neue Proteinmolek\u00fcle entworfen, die spannende M\u00f6glichkeiten f\u00fcr die Erkennung und Behandlung von Krankheiten er\u00f6ffnen.<\/strong><\/p>\n<p>Die Wirksamkeit eines Arzneimittels bei der Behandlung einer Krankheit h\u00e4ngt weitgehend davon ab, wie spezifisch es sich an das krankheitsverursachende Ziel anlagert und wie stark es sich an dieses Ziel anlagert.<\/p>\n<p>Die nahezu unendliche Anzahl m\u00f6glicher Proteinformen macht die Entwicklung eines Proteins mit hoher Affinit\u00e4t (Bindungsst\u00e4rke) und hoher Spezifit\u00e4t zu einer \u00e4u\u00dferst schwierigen Aufgabe.<\/p>\n<p>Die Suche nach Proteinen, die stark an helikale Peptidziele wie Glucagon, Neuropeptid Y und Parathormon binden, war die schwierige Aufgabe, bei der die Forscher hofften, dass KI sie unterst\u00fctzen k\u00f6nnte. Ihr Papier, <a href=\"https:\/\/www.nature.com\/articles\/s41586-023-06953-1\" target=\"_blank\" rel=\"noopener\">ver\u00f6ffentlicht in Nature<\/a>zeigt das Potenzial der KI f\u00fcr eine Revolutionierung <a href=\"https:\/\/dailyai.com\/de\/2023\/12\/astrazeneca-bets-247m-on-ai-company-developing-cancer-drug\/\">Arzneimittelentwicklung<\/a>.<\/p>\n<p>Die Forscher setzten zun\u00e4chst Deep-Learning-Halluzinationsmethoden mit AlphaFold2 und RosettaFold ein. Diese fortschrittlichen KI-Programme waren eine gro\u00dfe Hilfe bei der Entwicklung neuer Proteine, verbrauchen aber viel Rechenleistung.<\/p>\n<p>Auf der Suche nach einer rechnerisch effizienteren Methode f\u00fcr das Proteindesign wendeten sich die Forscher einem Ansatz zu, der dem \u00e4hnelt, mit dem KI-Bildgeneratormodelle wie Stable Diffusion und DALL-E arbeiten.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Papier heute <a href=\"https:\/\/twitter.com\/Nature?ref_src=twsrc%5Etfw\">@Natur<\/a> beschreibt <a href=\"https:\/\/twitter.com\/UWproteindesign?ref_src=twsrc%5Etfw\">@UWproteindesign<\/a> KI-generiertes Protein mit au\u00dfergew\u00f6hnlich hoher Bindungsaffinit\u00e4t und Spezifit\u00e4t. Diese Biotechnologie hat Auswirkungen auf die Arzneimittelentwicklung, die Erkennung von Krankheiten und die Umwelt\u00fcberwachung. <a href=\"https:\/\/t.co\/KhtGhiyf6I\">https:\/\/t.co\/KhtGhiyf6I<\/a><\/p>\n<p>- UW Medicine Newsroom (@uwmnewsroom) <a href=\"https:\/\/twitter.com\/uwmnewsroom\/status\/1736785659137016153?ref_src=twsrc%5Etfw\">18. Dezember 2023<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<h2>RF-Diffusion<\/h2>\n<p>RFdiffusion ist ein bahnbrechendes generatives KI-Modell, das auf Daten aus bekannten Proteinstrukturen trainiert wird. Das Modell verfeinert dann iterativ die Atome und ordnet sie in definierte Proteinstrukturen um.<\/p>\n<p>RFdiffusion wurde darauf trainiert, iterativ Rauschen aus Wolken unverbundener Atome zu entfernen und diese dann zu neuen Proteinstrukturen anzuordnen. Dieses Modell wurde in Verbindung mit dem vom Baker-Labor entwickelten Software-Tool ProteinMPNN verwendet, das Teil des <a href=\"https:\/\/www.ipd.uw.edu\/\" target=\"_blank\" rel=\"noopener\">Institut f\u00fcr Proteindesign<\/a> an der Universit\u00e4t von Washington.<\/p>\n<p>ProteinMPNN nimmt eine Proteinstruktur als Eingabe und verwendet Deep-Learning-Techniken, um schnell neue Aminos\u00e4uresequenzen zu identifizieren, die sich wahrscheinlich zu bestimmten Proteinstrukturen falten.<\/p>\n<h2>Spannende Ergebnisse<\/h2>\n<p>Die von den Forschern entwickelten Proteine wiesen eine au\u00dfergew\u00f6hnlich hohe Affinit\u00e4t und Spezifit\u00e4t f\u00fcr die Peptide auf, auf die sie ausgerichtet waren. Das bedeutet, dass sie m\u00f6glicherweise zur Entwicklung von Arzneimitteln verwendet werden k\u00f6nnten, die auf die Ursache einer Krankheit abzielen, anstatt an unbeabsichtigte Ziele zu binden und m\u00f6glicherweise Nebenwirkungen zu verursachen.<\/p>\n<p>Die Proteinsynthese ist nicht neu, aber dieser neue Ansatz lieferte Proteine, die die h\u00f6chste jemals gemeldete Interaktionsst\u00e4rke zwischen einem von einem Computer entworfenen Biomolek\u00fcl und seinem Ziel erreichen.<\/p>\n<p>David Baker, Professor f\u00fcr Biochemie an der UW Medicine und Forscher am Howard Hughes Medical Institute, war der Hauptautor der Forschungsarbeit.<\/p>\n<p>Baker erl\u00e4uterte die Bedeutung der Ergebnisse: \"Die F\u00e4higkeit, neuartige Proteine mit einer so hohen Bindungsaffinit\u00e4t und Spezifit\u00e4t zu erzeugen, er\u00f6ffnet eine Welt von M\u00f6glichkeiten, von neuen Krankheitsbehandlungen bis hin zu fortschrittlicher Diagnostik.\"<\/p>\n<p>Es gibt viele Krankheiten, die derzeit mit Antik\u00f6rpern behandelt werden. Die Herstellung von Antik\u00f6rpern ist jedoch teuer und sie sind nicht lange haltbar.<\/p>\n<p>Preetham Venkatesh, einer der leitenden Forscher, sagte: \"Es gibt viele Krankheiten, die heute nur schwer zu behandeln sind, weil es so schwierig ist, bestimmte Molek\u00fcle im K\u00f6rper nachzuweisen. Als Diagnoseinstrumente k\u00f6nnten designte Proteine eine kosteng\u00fcnstigere Alternative zu Antik\u00f6rpern darstellen.\"<\/p>\n<p>Die Forscher konnten ihre Biodesign-Methoden validieren, indem sie Labortests in Zusammenarbeit mit dem Joseph Rogers Lab an der Universit\u00e4t Kopenhagen und dem Andrew Hoofnagle Lab an der UW Medicine durchf\u00fchrten.<\/p>\n<p>Diese Forschung ist ein gro\u00dfartiges Beispiel daf\u00fcr, wie die KI die Entwicklung neuer Behandlungsmethoden f\u00fcr Krankheiten beschleunigt. Die Arbeit muss noch von Experten begutachtet werden, aber die ersten Ergebnisse sind sehr spannend.<\/p>","protected":false},"excerpt":{"rendered":"<p>Wissenschaftler der University of Washington School of Medicine haben mithilfe der k\u00fcnstlichen Intelligenz neue Proteinmolek\u00fcle entwickelt, die spannende M\u00f6glichkeiten f\u00fcr die Erkennung und Behandlung von Krankheiten er\u00f6ffnen. Die Wirksamkeit eines Medikaments bei der Behandlung einer Krankheit h\u00e4ngt weitgehend davon ab, wie spezifisch es sich an das krankheitsverursachende Ziel anlagert und wie stark es sich an das Ziel anlagert. Die nahezu unendliche Zahl m\u00f6glicher Proteinformen macht die Entwicklung eines Proteins mit hoher Affinit\u00e4t (Bindungsst\u00e4rke) und hoher Spezifit\u00e4t zu einer \u00e4u\u00dferst schwierigen Aufgabe. Die Suche nach Proteinen, die stark an helikale Peptidziele wie Glucagon, Neuropeptid Y und die Nebenschilddr\u00fcse<\/p>","protected":false},"author":6,"featured_media":8468,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[150,203,101],"class_list":["post-8436","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-benefits","tag-biotech","tag-medtech"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI-designed proteins display exceptional binding strengths | 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\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-designed proteins display exceptional binding strengths | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Scientists at the University of Washington School of Medicine used AI to design new protein molecules in a way that opens up exciting possibilities for the detection and treatment of diseases. The effectiveness of a drug in treating a disease depends largely on how specific it is in attaching to the disease-causing target, and how strongly it attaches to the target. The near-infinite amount of potential protein shapes makes designing a protein with both high affinity (binding strength) and high specificity an extremely difficult task. 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