تقرير
Improved motif-scaffolding with SE(3) flow matching
| العنوان: | Improved motif-scaffolding with SE(3) flow matching |
|---|---|
| المؤلفون: | Yim, Jason, Campbell, Andrew, Mathieu, Emile, Foong, Andrew Y. K., Gastegger, Michael, Jiménez-Luna, José, Lewis, Sarah, Satorras, Victor Garcia, Veeling, Bastiaan S., Noé, Frank, Barzilay, Regina, Jaakkola, Tommi S. |
| المصدر: | Transactions on Machine Learning Research 2024 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology Statistics |
| مصطلحات موضوعية: | Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models have achieved breakthrough success in designing scaffolds for a range of motifs. However, generated scaffolds tend to lack structural diversity, which can hinder success in wet-lab validation. In this work, we extend FrameFlow, an SE(3) flow matching model for protein backbone generation, to perform motif-scaffolding with two complementary approaches. The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy. The second is motif guidance, which performs scaffolding using an estimate of the conditional score from FrameFlow without additional training. On a benchmark of 24 biologically meaningful motifs, we show our method achieves 2.5 times more designable and unique motif-scaffolds compared to state-of-the-art. Code: https://github.com/microsoft/protein-frame-flow Comment: Preprint. Code: https://github.com/ microsoft/frame-flow |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2401.04082 |
| رقم الأكسشن: | edsarx.2401.04082 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |