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Paper Title

gRNAde: Geometric Deep Learning for 3D RNA inverse design

Authors

RISHABH ANAND
RISHABH ANAND
Ramon Vinas Torne
Ramon Vinas Torne
Arian R. Jamasb
Arian R. Jamasb
Simon Mathis
Simon Mathis
Chaitanya K. Joshi
Chaitanya K. Joshi

Article Type

Research Article

Journal

bioRxiv

Research Impact Tools

Issue

| Page No : 1-23

Published On

October, 2024

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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. 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. Open source code: github.com/chaitjo/geometric-rna-design

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