gRNAde: Geometric Deep Learning for 3D RNA inverse design
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929729476493312 |
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| author | Joshi, Chaitanya K. Jamasb, Arian R. Viñas, Ramon Harris, Charles Mathis, Simon V. Morehead, Alex Anand, Rishabh Liò, Pietro |
| author_facet | Joshi, Chaitanya K. Jamasb, Arian R. Viñas, Ramon Harris, Charles Mathis, Simon V. Morehead, Alex Anand, Rishabh Liò, Pietro |
| contents | 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 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. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to 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 ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that gRNAde has a success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: https://github.com/chaitjo/geometric-rna-design |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_14749 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | gRNAde: Geometric Deep Learning for 3D RNA inverse design Joshi, Chaitanya K. Jamasb, Arian R. Viñas, Ramon Harris, Charles Mathis, Simon V. Morehead, Alex Anand, Rishabh Liò, Pietro Machine Learning Biomolecules Quantitative Methods 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 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. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to 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 ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that gRNAde has a success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: https://github.com/chaitjo/geometric-rna-design |
| title | gRNAde: Geometric Deep Learning for 3D RNA inverse design |
| topic | Machine Learning Biomolecules Quantitative Methods |
| url | https://arxiv.org/abs/2305.14749 |