{"id":44602,"date":"2021-11-04T12:18:25","date_gmt":"2021-11-04T11:18:25","guid":{"rendered":"https:\/\/www.h-its.org\/de\/?p=44602"},"modified":"2021-11-05T10:12:59","modified_gmt":"2021-11-05T09:12:59","slug":"trapp4-release","status":"publish","type":"post","link":"https:\/\/www.h-its.org\/de\/2021\/11\/04\/trapp4-release\/","title":{"rendered":"Neue Version von TRAPP ver\u00f6ffentlicht"},"content":{"rendered":"\n

Forscher\/-innen der Molecular and Cellular Modeling (MCM)<\/a> Gruppe am HITS haben eine neue Version des Webservers TRA<\/strong>nsient P<\/strong>ockets in P<\/strong>roteins (TRAPP) ver\u00f6ffentlicht: https:\/\/trapp.h-its.org\/<\/a><\/p>\n\n\n\n

Die wichtigste Neuerung in TRAPP Version 4 ist der Einsatz von maschinellem Lernen und molekularer Simulation zur Vorhersage der \u201cDruggability\u201c, d. h. der F\u00e4higkeit der untersuchten Proteinbindungsstelle, an eine arzneistoff\u00e4hnliche Verbindung zu binden. Auf diese Weise erleichtert TRAPP die Entdeckung kryptischer Bindungstaschen, die in der experimentell ermittelten Struktur eines Proteins m\u00f6glicherweise nicht sichtbar sind, aber durch die Simulation der Proteinbewegungen aufgedeckt werden k\u00f6nnen.<\/p>\n\n\n\n

Der sogenannte Druggability-Score kann den Benutzer\/-innen helfen, aus einer Reihe von Konformationen, die aus Experimenten oder einer Molekulardynamiksimulation gewonnen wurden, die am meisten \u201edruggable\u201c Konformation einer bestimmten Bindungstasche zu identifizieren.  Diese vorhergesagte druggable Konformation kann dann f\u00fcr das Design von Wirkstoffen verwendet werden. TRAPP enth\u00e4lt auch Visualisierungswerkzeuge, die es den Benutzer\/-innen erm\u00f6glichen, die Faktoren zu identifizieren, die sich auf die vorhergesagte Druggability von Proteinbindetaschen auswirken.<\/p>\n\n\n\n

Weitere Quellen:<\/strong><\/p>\n\n\n\n

Jui-Hung Yuan, Sungho Bosco Han, Stefan Richter, Rebecca C Wade, Daria B. Kokh
Druggability Assessment in TRAPP using Machine Learning Approaches
JYuan J, Han SB, Richter S, Wade RC, Kokh DB (2020). Druggability Assessment in TRAPP Using Machine Learning Approaches<\/a>, J. Chem. Inf. Model. 60(3):1685-1699 1<\/p>\n\n\n\n

Stank A, Kokh DB, Horn M, Sizikova E, Neil R, Panecka J, Richter S, Wade RC (2017). TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets.<\/a>, Nucleic Acids Research 45(W1):W325-W330<\/p>\n\n\n\n

Kokh DB, Richter S, Henrich S, Czodrowski P, Rippmann F, Wade RC (2013). TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins<\/a>, J. Chem. Inf. Model. 53(5):1235-1252<\/p>\n","protected":false},"excerpt":{"rendered":"

Forscher\/-innen der Molecular and Cellular Modeling (MCM) Gruppe am HITS haben eine neue Version des Webservers TRAnsient Pockets in Proteins (…<\/p>\n","protected":false},"author":6,"featured_media":44603,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1421],"hits-research-group":[],"class_list":["post-44602","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software-news"],"acf":[],"yoast_head":"\nNeue Version von TRAPP ver\u00f6ffentlicht - HITS gGmbH<\/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:\/\/www.h-its.org\/de\/2021\/11\/04\/trapp4-release\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Neue Version von TRAPP ver\u00f6ffentlicht - 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