Skip to content
#protein folding Review Open access

Engineering Allosteric RNA Aptamers: Molecular Mechanisms, Functional Devices, and Translational Applications

Oct 2026 · Digital Medical Engineering · 0 citations · 110 references

Abstract

Allostery enables selective and context-dependent regulation of biological function. Although classically associated with proteins, RNA also samples dynamic conformational ensembles in which ligand binding can be coupled to a spatially or functionally distinct output. Allosteric RNA aptamers harness this property to convert molecular inputs into fluorescence, catalysis, RNA processing, translational control, macromolecular recruitment, or therapeutic activity. Their modularity, genetic encodability, and intracellular functionality position them at the intersection of chemical biology, synthetic biology, functional genomics, and RNA medicine. This Review examines the mechanisms of RNA allostery, strategies for engineering ligand-responsive architectures, and applications in live-cell sensing, drug discovery, and therapeutic regulation. Representative systems are evaluated by target, structure, affinity, dynamic range, output modality, and application. We highlight aptazymes, conditional gene-control modules, biomarker-responsive circuits, and RNA-scaffolded proximity-inducing systems, and compare RNA devices with DNA aptamers, DNAzymes, and switches. Key translational barriers include context-dependent folding, intracellular instability, delivery, pharmacokinetics and pharmacodynamics, immunogenicity, and inadequate manufacturing and assay standardization. Advances in ensemble-resolved structural biology, high-throughput evolution, artificial intelligence-assisted design, and RNA delivery may establish allosteric aptamers as programmable pharmacological systems coupling molecular recognition to spatiotemporally controlled therapeutic activity.

Read PDF

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.