تقرير
gRNAde: Geometric Deep Learning for 3D RNA inverse design
| العنوان: | gRNAde: Geometric Deep Learning for 3D RNA inverse design |
|---|---|
| المؤلفون: | Joshi, Chaitanya K., Jamasb, Arian R., Viñas, Ramon, Harris, Charles, Mathis, Simon V., Morehead, Alex, Anand, Rishabh, Liò, Pietro |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Quantitative Biology - Biomolecules, Quantitative Biology - Quantitative Methods |
| الوصف: | Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. Under the hood, gRNAde is a multi-state Graph Neural Network that generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. [2010], gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent RNA polymerase ribozyme structure. Open source code: https://github.com/chaitjo/geometric-rna-design Comment: Previously titled 'Multi-State RNA Design with Geometric Multi-Graph Neural Networks', presented at ICML 2023 Computational Biology Workshop |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2305.14749 |
| رقم الأكسشن: | edsarx.2305.14749 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |