gRNAde: Geometric Deep Learning for 3D RNA inverse design

• URL: http://arxiv.org/abs/2305.14749v6
• Date: Sun, 06 Oct 2024 06:39:41 GMT
• Title: gRNAde: Geometric Deep Learning for 3D RNA inverse design
• Authors: Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas, Charles Harris, Simon V. Mathis, Alex Morehead, Rishabh Anand, Pietro Liò,
• Abstract summary: gRNAde is a geometric RNA design pipeline operating on 3D RNA backbones. It generates sequences that explicitly account for structure and dynamics.
• Score: 14.729049204432027
• License:
• Abstract: 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 ribozyme. Open source code: https://github.com/chaitjo/geometric-rna-design

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