{"id":49343,"date":"2024-10-15T14:20:08","date_gmt":"2024-10-15T12:20:08","guid":{"rendered":"https:\/\/www.h-its.org\/de\/?post_type=hits-project&p=49343"},"modified":"2024-10-16T11:08:55","modified_gmt":"2024-10-16T09:08:55","slug":"diffusion-models","status":"publish","type":"hits-project","link":"https:\/\/www.h-its.org\/de\/projects\/diffusion-models\/","title":{"rendered":"Diffusion models for protein design"},"content":{"rendered":"
\n
\"Diffusion_Models\"<\/a>
Das Projekt hat zum Ziel, diese Vernetzungen (hier dargestellt als Raumf\u00fcllung, modelliert in Kollagen) in einem neu entworfenen De-novo-Proteinger\u00fcst durch neuartige, verbesserte Str\u00f6mungsanpassungsmodelle zu erzeugen<\/em>.<\/figcaption><\/figure>\n<\/div>\n\n\n

Generatives Deep Learning hat das Proteindesign ver\u00e4ndert und erm\u00f6glicht die Entwicklung neuer Proteinstrukturen, Interaktionen und Funktionen mit hoher Pr\u00e4zision, durch generative Modelle wie Diffusionsmodelle und Flow Matching. Geometrisches Deep Learning spielt eine entscheidende Rolle, da es die Modellierung von Symmetrien erm\u00f6glicht, um die Aussagekraft und Effizienz des Modells zu optimieren.<\/p>\n\n\n\n

In diesem Projekt zielen wir darauf ab, aktuelle generative Modelle f\u00fcr das Proteindesign zu verbessern, indem wir die Art und Weise, wie \u00c4quivarianz in sie eingebaut wird, \u00fcberdenken und diese Erkenntnisse in den generativen Prozess einbeziehen. Wir befassen uns speziell mit dem Design eines Proteins mit einer dreiwertigen Querverbindung: einer komplexen chemischen Bindung zwischen drei Aminos\u00e4uren. Eine solche Struktur wurde noch nicht erfolgreich synthetisiert, es ist aber bekannt, dass sie in Kollagen vorkommt. Diese Herausforderung nehmen wir an und wollen damit sowohl die Diffusionsmodelle verbessern als auch unser Verst\u00e4ndnis dieses komplizierten biochemischen Prozesses vertiefen.<\/p>\n\n\n\n

Beteiligte: <\/strong>
Frauke Gr\u00e4ter<\/a> (MBM)
Jan St\u00fchmer<\/a> (MLI)
<\/p>\n\n\n\n

Mitglieder:<\/strong>
Leif Seute<\/a>
Vsevolod Viliuga
Simon Wagner<\/a>
Nicolas Wolf<\/p>\n\n\n\n

Publikationen:<\/strong>
Seute et al.: Grappa \u2014 A Machine Learned Molecular Mechanics Force Field
https:\/\/arxiv.org\/abs\/2404.00050<\/a>

Wagner S, Seute L, Viliuga V, Wolf N, Gr\u00e4ter F, St\u00fchmer J: Generating Highly Designable Proteins with Geometric Algebra Flow Matching.
The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 (accepted).<\/p>\n\n\n\n

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