{"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\/da\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/","title":{"rendered":"AI-designede proteiner viser exceptionelle bindingsstyrker"},"content":{"rendered":"<p><strong>Forskere ved University of Washington School of Medicine brugte AI til at designe nye proteinmolekyler p\u00e5 en m\u00e5de, der \u00e5bner sp\u00e6ndende muligheder for at opdage og behandle sygdomme.<\/strong><\/p>\n<p>Hvor effektivt et l\u00e6gemiddel er til at behandle en sygdom, afh\u00e6nger i h\u00f8j grad af, hvor specifikt det binder sig til det sygdomsfremkaldende m\u00e5l, og hvor st\u00e6rkt det binder sig til m\u00e5let.<\/p>\n<p>Den n\u00e6sten uendelige m\u00e6ngde af potentielle proteinformer g\u00f8r det til en ekstremt vanskelig opgave at designe et protein med b\u00e5de h\u00f8j affinitet (bindingsstyrke) og h\u00f8j specificitet.<\/p>\n<p>At finde proteiner, der binder st\u00e6rkt til spiralformede peptidm\u00e5l som glukagon, neuropeptid Y og parathyreoideahormon, var den udfordrende opgave, som forskerne h\u00e5bede, at AI kunne hj\u00e6lpe med. Deres artikel, <a href=\"https:\/\/www.nature.com\/articles\/s41586-023-06953-1\" target=\"_blank\" rel=\"noopener\">udgivet i Nature<\/a>viser potentialet for, at AI kan revolutionere <a href=\"https:\/\/dailyai.com\/da\/2023\/12\/astrazeneca-bets-247m-on-ai-company-developing-cancer-drug\/\">udvikling af l\u00e6gemidler<\/a>.<\/p>\n<p>Forskerne brugte f\u00f8rst deep learning-hallucinationsmetoder med AlphaFold2 og RosettaFold. Disse avancerede AI-programmer har v\u00e6ret en stor hj\u00e6lp til at designe nye proteiner, men de bruger meget computerkraft.<\/p>\n<p>I et fors\u00f8g p\u00e5 at finde en mere beregningseffektiv proteindesignmetode vendte forskerne sig mod en tilgang, der ligner den, der driver AI-billedgeneratormodeller som Stable Diffusion og DALL-E.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Papir i dag <a href=\"https:\/\/twitter.com\/Nature?ref_src=twsrc%5Etfw\">@Natur<\/a> beskriver <a href=\"https:\/\/twitter.com\/UWproteindesign?ref_src=twsrc%5Etfw\">@UWproteindesign<\/a> AI-genereret protein med us\u00e6dvanlig h\u00f8j bindingsaffinitet og specificitet. Denne bioteknologi har betydning for l\u00e6gemiddeludvikling, sygdomsdetektion og milj\u00f8overv\u00e5gning. <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. december 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 er en banebrydende generativ AI-model, der tr\u00e6nes p\u00e5 data fra kendte proteinstrukturer. Modellen forfiner og omarrangerer derefter iterativt atomer til definerede proteinstrukturer.<\/p>\n<p>RFdiffusion blev tr\u00e6net til iterativt at fjerne st\u00f8j fra skyer af frakoblede atomer og derefter omarrangere dem til nye proteinstrukturer. Denne model blev brugt sammen med softwarev\u00e6rkt\u00f8jet ProteinMPNN, der er designet af Baker Lab, som er en del af <a href=\"https:\/\/www.ipd.uw.edu\/\" target=\"_blank\" rel=\"noopener\">Institut for Proteindesign<\/a> p\u00e5 University of Washington.<\/p>\n<p>ProteinMPNN tager en proteinstruktur som input og bruger deep learning-teknikker til hurtigt at identificere nye aminosyresekvenser, som sandsynligvis vil folde sig ind i specifikke proteinstrukturer.<\/p>\n<h2>Sp\u00e6ndende resultater<\/h2>\n<p>De proteiner, forskerne designede, udviste us\u00e6dvanlig h\u00f8j affinitet og specificitet over for de peptider, de var rettet mod. Det betyder, at de potentielt kan bruges til at skabe l\u00e6gemidler, der retter sig mod \u00e5rsagen til en sygdom i stedet for at binde sig til utilsigtede m\u00e5l og potentielt for\u00e5rsage bivirkninger.<\/p>\n<p>Proteinsyntese er ikke noget nyt, men denne nye tilgang leverede proteiner, der opn\u00e5ede den h\u00f8jeste interaktionsstyrke, der nogensinde er rapporteret mellem et biomolekyle designet af en computer og dets m\u00e5l.<\/p>\n<p>David Baker, professor i biokemi ved UW Medicine og forsker ved Howard Hughes Medical Institute, var hovedforfatter til forskningsartiklen.<\/p>\n<p>Baker forklarede betydningen af resultaterne og sagde: \"Evnen til at generere nye proteiner med s\u00e5 h\u00f8j bindingsaffinitet og specificitet \u00e5bner op for en verden af muligheder, fra nye sygdomsbehandlinger til avanceret diagnostik.\"<\/p>\n<p>Der er mange sygdomme, som i dag behandles med antistoffer. Men antistoffer er dyre at producere og har ikke lang holdbarhed.<\/p>\n<p>Preetham Venkatesh, en af de ledende forskere, siger: \"Der er mange sygdomme, som er sv\u00e6re at behandle i dag, simpelthen fordi det er s\u00e5 udfordrende at opdage visse molekyler i kroppen. Som redskaber til diagnosticering kan designede proteiner v\u00e6re et mere omkostningseffektivt alternativ til antistoffer.\"<\/p>\n<p>Forskerne var i stand til at validere deres biodesign-metoder ved at udf\u00f8re laboratorietest i samarbejde med Joseph Rogers Lab p\u00e5 K\u00f8benhavns Universitet og Andrew Hoofnagle Lab p\u00e5 UW Medicine.<\/p>\n<p>Denne forskning er et godt eksempel p\u00e5, hvordan kunstig intelligens fremskynder udviklingen af nye behandlinger af sygdomme. Artiklen er endnu ikke blevet fagf\u00e6llebed\u00f8mt, men de f\u00f8rste resultater er meget sp\u00e6ndende.<\/p>","protected":false},"excerpt":{"rendered":"<p>Forskere ved University of Washington School of Medicine brugte AI til at designe nye proteinmolekyler p\u00e5 en m\u00e5de, der \u00e5bner sp\u00e6ndende muligheder for at opdage og behandle sygdomme. Et l\u00e6gemiddels effektivitet i behandlingen af en sygdom afh\u00e6nger i h\u00f8j grad af, hvor specifikt det binder sig til det sygdomsfremkaldende m\u00e5l, og hvor st\u00e6rkt det binder sig til m\u00e5let. Den n\u00e6sten uendelige m\u00e6ngde af potentielle proteinformer g\u00f8r det til en ekstremt vanskelig opgave at designe et protein med b\u00e5de h\u00f8j affinitet (bindingsstyrke) og h\u00f8j specificitet. At finde proteiner, der binder st\u00e6rkt til helikale peptidm\u00e5l som glukagon, neuropeptid Y og parathyroid<\/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\/da\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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. 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