Designing RNA Machines: Computational and Experimental Approaches to Engineering Ribozymes, Non-Coding RNAs, and RNA-Based Regulatory Networks

RNA, in addition to being an information carrying molecule, has the capacity to fold into three-dimensional structures and carry out catalytic and regulatory functions, which has made it one of the most versatile molecules in biology — and one of the most promising platforms for biotechnological and therapeutic engineering. Yet the relationship between RNA sequence, structure, and function remains incompletely understood, and the rational design of RNA constructs with predictable activity continues to challenge researchers across disciplines.Recent advances in computational RNA biology have begun to transform how ribozymes, aptamers, and non-coding RNA regulators are conceived, optimized, and deployed. Thermodynamic ensemble modeling and minimum free energy prediction now allow researchers to interrogate the folding landscape of candidate sequences at scale, identifying constructs whose predicted secondary structures are compatible with catalytic or regulatory function before a single experiment is performed. Equally important is the complementary problem of inverse RNA folding: rather than predicting the structure a given sequence will adopt, inverse folding algorithms take a desired target structure as input and search sequence space for solutions that fold reliably into it. This capacity to design sequences toward a structural specification, rather than merely predict structure from sequence, is central to the rational engineering of functional RNA constructs with defined active-site geometries, conserved catalytic motifs, or precisely positioned binding pockets.Beyond secondary structure, the prediction and design of RNA tertiary architecture has advanced substantially with the emergence of deep learning tools which now enable in silico modeling of full three-dimensional RNA folds. Concurrently, coarse-grained molecular dynamics simulations and large-scale in vitro selection coupled with high-throughput sequencing are providing sequence-activity landscapes that serve both as training data for machine learning models and as experimental benchmarks against which computational predictions can be tested. These computational advances, however, must be grounded in rigorous wet-bench validation, and the productive dialogue between in silico design and experimental characterization remains both the central challenge and the most generative frontier of the field.This Research Topic invites contributions that deal with (but are not limited to) the following areas:- Computational design and characterization of trans-acting ribozymes as gene-silencing or mRNA-targeting tools- De novo RNA structure design via inverse folding, thermodynamic ensemble modeling, or deep learning- Machine learning approaches to prediction of ribozyme activity, aptamer binding, or riboswitch function- Computational prediction and experimental mapping of RNA–RNA and RNA–protein interaction networks- Design and characterization of synthetic RNA-based devices such as riboswitches, RNA logic gates, and programmable RNA devices- Benchmarking of RNA structure prediction tools against SHAPE-MaP, DMS-MaPseq, or cryo-EM data- Molecular dynamics and coarse-grained simulation of ribozyme dynamics, catalysis, and metal ion coordination- Closed-loop workflows coupling computational RNA design with experimental validation and model refinement- Aptamer and biosensor design and discovery via computational means and SELEX coupled with high-throughput sequencing- Structural and functional studies of ncRNAs (including lncRNAs, circRNAs, and small regulatory RNAs)- Modeling of RNA World systems, protocellular ribozymes, and autocatalytic RNA networks- Methodological advances in RNA design software, simulation, and high-throughput functional characterization. Topic editors: Nawwaf Kharma, Jonathan Perreault Source: https://www.frontiersin.org/research-topics/84445/designing-rna-machines-computational-and-experimental-approaches-to-engineering-ribozymes-non-coding-rnas-and-rna-based-regulatory-networks