gRNAde: Geometric Deep Learning for 3D RNA inverse design
Keywords: RNA Structure, RNA Design, Geometric Deep Learning, Graph Neural Networks
TL;DR: GNN-based, wet-lab validated 3D RNA design pipeline; obtains SOTA performance for single-state, multi-state design, mutant fitness ranking.
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 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 an impressive success rate of 50%, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: https://anonymous.4open.science/r/geometric-rna-design
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 9487
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