gRNAde: Geometric Deep Learning for 3D RNA inverse design
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 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
Keywords
Cite
@article{arxiv.2305.14749,
title = {gRNAde: Geometric Deep Learning for 3D RNA inverse design},
author = {Chaitanya K. Joshi and Arian R. Jamasb and Ramon Viñas and Charles Harris and Simon V. Mathis and Alex Morehead and Rishabh Anand and Pietro Liò},
journal= {arXiv preprint arXiv:2305.14749},
year = {2025}
}
Comments
ICLR 2025 camera-ready version (Spotlight presentation). Previously titled 'Multi-State RNA Design with Geometric Multi-Graph Neural Networks', presented at ICML 2023 Computational Biology Workshop