{"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\/it\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/","title":{"rendered":"Le proteine progettate dall'AI mostrano un'eccezionale forza di legame"},"content":{"rendered":"<p><strong>Gli scienziati della University of Washington School of Medicine hanno utilizzato l'intelligenza artificiale per progettare nuove molecole proteiche, aprendo cos\u00ec interessanti possibilit\u00e0 per il rilevamento e il trattamento delle malattie.<\/strong><\/p>\n<p>L'efficacia di un farmaco nel trattamento di una malattia dipende in larga misura dalla sua specificit\u00e0 nell'attaccarsi al bersaglio della malattia e dalla forza con cui si attacca al bersaglio.<\/p>\n<p>La quantit\u00e0 quasi infinita di forme potenziali di proteine rende estremamente difficile la progettazione di una proteina con un'elevata affinit\u00e0 (forza di legame) e specificit\u00e0.<\/p>\n<p>Trovare proteine che si legano fortemente a bersagli peptidici elicoidali come il glucagone, il neuropeptide Y e l'ormone paratiroideo \u00e8 stato il compito arduo che i ricercatori speravano potesse essere svolto dall'intelligenza artificiale. Il loro articolo, <a href=\"https:\/\/www.nature.com\/articles\/s41586-023-06953-1\" target=\"_blank\" rel=\"noopener\">pubblicato su Nature<\/a>mostra il potenziale di rivoluzione dell'IA <a href=\"https:\/\/dailyai.com\/it\/2023\/12\/astrazeneca-bets-247m-on-ai-company-developing-cancer-drug\/\">sviluppo del farmaco<\/a>.<\/p>\n<p>I ricercatori hanno utilizzato per la prima volta i metodi di allucinazione dell'apprendimento profondo con AlphaFold2 e RosettaFold. Questi programmi avanzati di intelligenza artificiale sono stati di grande aiuto nella progettazione di nuove proteine, ma utilizzano molta potenza di calcolo.<\/p>\n<p>Nel tentativo di trovare un metodo di progettazione delle proteine pi\u00f9 efficiente dal punto di vista computazionale, i ricercatori si sono rivolti a un approccio simile a quello che alimenta modelli di generatori di immagini AI come Stable Diffusion e DALL-E.<\/p>\n<blockquote class=\"twitter-tweet\">\n<p dir=\"ltr\" lang=\"en\">Carta di oggi <a href=\"https:\/\/twitter.com\/Nature?ref_src=twsrc%5Etfw\">@Natura<\/a> descrive <a href=\"https:\/\/twitter.com\/UWproteindesign?ref_src=twsrc%5Etfw\">@UWproteindesign<\/a> Proteine generate dall'IA con affinit\u00e0 e specificit\u00e0 di legame eccezionalmente elevate. Questa biotecnologia ha implicazioni per lo sviluppo di farmaci, il rilevamento di malattie e il monitoraggio ambientale. <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 dicembre 2023<\/a><\/p><\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<h2>RFdiffusione<\/h2>\n<p>RFdiffusion \u00e8 un innovativo modello di intelligenza artificiale generativa che viene addestrato su dati provenienti da strutture proteiche note. Il modello poi perfeziona e riorganizza iterativamente gli atomi in strutture proteiche definite.<\/p>\n<p>RFdiffusion \u00e8 stato addestrato a rimuovere iterativamente il rumore dalle nuvole di atomi scollegati, per poi riorganizzarle in nuove strutture proteiche. Questo modello \u00e8 stato utilizzato in combinazione con lo strumento software ProteinMPNN, progettato dal Baker Lab, parte del <a href=\"https:\/\/www.ipd.uw.edu\/\" target=\"_blank\" rel=\"noopener\">Istituto per la progettazione di proteine<\/a> presso l'Universit\u00e0 di Washington.<\/p>\n<p>ProteinMPNN prende in input una struttura proteica e utilizza tecniche di deep learning per identificare rapidamente nuove sequenze di amminoacidi che probabilmente si ripiegano in specifiche strutture proteiche.<\/p>\n<h2>Risultati entusiasmanti<\/h2>\n<p>Le proteine progettate dai ricercatori hanno mostrato un'affinit\u00e0 e una specificit\u00e0 eccezionalmente elevate per i peptidi che avevano come bersaglio. Ci\u00f2 significa che potenzialmente potrebbero essere utilizzate per creare farmaci che mirano alla causa di una malattia piuttosto che legarsi a bersagli non previsti e causare effetti collaterali.<\/p>\n<p>La sintesi proteica non \u00e8 una novit\u00e0, ma questo nuovo approccio ha fornito proteine che hanno raggiunto la pi\u00f9 alta forza di interazione mai registrata tra una biomolecola progettata da un computer e il suo bersaglio.<\/p>\n<p>David Baker, professore di biochimica presso la UW Medicine e ricercatore presso l'Howard Hughes Medical Institute, \u00e8 stato l'autore principale della ricerca.<\/p>\n<p>Baker ha spiegato l'importanza dei risultati dicendo: \"La capacit\u00e0 di generare nuove proteine con un'affinit\u00e0 e una specificit\u00e0 di legame cos\u00ec elevate apre un mondo di possibilit\u00e0, dai trattamenti di nuove malattie alla diagnostica avanzata\".<\/p>\n<p>Sono molte le malattie che attualmente vengono trattate con gli anticorpi. Tuttavia, gli anticorpi sono costosi da produrre e non hanno una grande durata di conservazione.<\/p>\n<p>Preetham Venkatesh, uno dei ricercatori principali, ha dichiarato: \"Ci sono molte malattie che oggi sono difficili da trattare semplicemente perch\u00e9 \u00e8 cos\u00ec impegnativo rilevare certe molecole nel corpo. Come strumenti di diagnosi, le proteine progettate possono offrire un'alternativa pi\u00f9 economica agli anticorpi\".<\/p>\n<p>I ricercatori hanno potuto convalidare i loro metodi di bioprogettazione conducendo test di laboratorio in collaborazione con il Joseph Rogers Lab dell'Universit\u00e0 di Copenaghen e l'Andrew Hoofnagle Lab della UW Medicine.<\/p>\n<p>Questa ricerca \u00e8 un ottimo esempio di come l'intelligenza artificiale stia accelerando lo sviluppo di nuovi trattamenti per le malattie. Il documento deve ancora essere sottoposto a revisione paritaria, ma i risultati iniziali sono molto interessanti.<\/p>","protected":false},"excerpt":{"rendered":"<p>Gli scienziati della University of Washington School of Medicine hanno utilizzato l'intelligenza artificiale per progettare nuove molecole proteiche in un modo che apre interessanti possibilit\u00e0 per il rilevamento e il trattamento delle malattie. L'efficacia di un farmaco nel trattamento di una malattia dipende in larga misura dalla sua specificit\u00e0 nell'attaccarsi al bersaglio della malattia e dalla forza con cui si attacca al bersaglio. La quantit\u00e0 quasi infinita di forme potenziali di proteine rende estremamente difficile la progettazione di una proteina con un'affinit\u00e0 (forza di legame) e una specificit\u00e0 elevate. Trovare proteine che si leghino fortemente a bersagli peptidici elicoidali come il glucagone, il neuropeptide Y e le paratiroidi \u00e8 un compito estremamente difficile.<\/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\/it\/2023\/12\/ai-designed-proteins-display-exceptional-binding-strengths\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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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