Efficient Design of RNA Sequences with Desired Properties, Structure, and Motifs Using A Grammar Variational Autoencoder

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ID: 331463
2026
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Abstract
Abstract Designing structurally stable RNA sequences with specific motifs and other desirable properties is an important challenge in bioinformatics. The potential design space increases exponentially with the length of the RNA to be engineered, making this a difficult combinatorial optimization problem. In this paper, we propose an RNA grammar variational autoencoder (RGVAE) that can efficiently generate novel RNA sequences with specific target properties. The proposed RGVAE builds on the recently proposed grammar VAE, where we incorporate the stochastic context-free grammar (SCFG) to design structural RNAs with desired motifs and characteristics. Using the SCFG can ensure that the generated RNA sequence can form a thermodynamically stable secondary structure. Given a RNA sequence, the SCFG is used to find the parse tree, which is represented in a continuous low-dimensional latent space by the RGVAE encoder. We can optimize the RNA in the latent space, where the latent representation can be decoded by the RGVAE decoder to reconstruct the RNA sequence. Based on a number of practical uses cases, we demonstrate that RGVAE can be used to efficiently design structurally stable RNAs with specific target properties compared to other alternative approaches that do not explicitly incorporate SCFG into the design process. Code availability: The source code of RGVAE and the data used in this study can be found at: https://github.com/nzarnaghinaghsh/RGVAE.
Reference Key
openalex_W4414384912 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Narges Zarnaghinaghsh, Byung-Jun Yoon
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag298
URL
https://doi.org/10.1093/bioadv/vbag298
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