Ribonucleic acid, more commonly known as RNA, has emerged as a molecular superstar in the world of biology, far surpassing its traditional role as a mere courier of genetic instructions. Its ability to fold into intricate three-dimensional forms underpins a diverse array of biological functions, from gene regulation to maintaining cellular homeostasis. This structural versatility has propelled RNA to the forefront of biotechnology and therapeutic development, especially with the rapid progress of RNA-based vaccines and gene-editing technologies. However, accurately predicting the folding pathways and final structures of RNA molecules remains an elusive goal that challenges computational biologists worldwide.
Folding of RNA into stable and functional configurations involves complex intramolecular interactions that yield characteristic secondary and tertiary structures. These structures are critical because they dictate RNA’s ability to interact with other biomolecules and execute its biological roles. While experimental methods such as X-ray crystallography and nuclear magnetic resonance can provide snapshots of these structures, they are labor-intensive and sometimes fail to capture dynamic folding processes. Consequently, molecular dynamics (MD) simulations have become a powerful computational tool for investigating RNA folding, enabling researchers to model the movement of atoms over time under defined physical laws.
Despite advances, simulating the full folding process of RNA molecules starting from an unfolded chain to their native conformation remains notoriously difficult. Standard MD simulations require extensive computational resources due to the sheer number of atoms involved and the prolonged timescales needed to observe folding, often beyond what is feasible with explicit solvent models where every water molecule and ion is individually represented. This limitation has historically confined successful folding simulations to small, simple RNA motifs, typically short stem-loop structures comprising approximately ten nucleotides.
In this groundbreaking research spearheaded by Associate Professor Tadashi Ando at Tokyo University of Science, Japan, a paradigm shift in RNA folding simulations has been achieved. The study employed a hybrid computational approach, combining an advanced atomistic force field named DESRES-RNA, which meticulously represents atomic interactions in RNA molecules, with the GB-neck2 generalized Born implicit solvent model. This solvent model abstracts the aqueous environment as a continuous medium rather than discrete molecules, significantly accelerating the conformational sampling process without substantial compromise in accuracy.
Dr. Ando’s team applied this innovative computational framework to an unprecedentedly diverse library of 26 RNA stem-loop constructs. These molecules varied broadly in size, from 10 to 36 nucleotides, and included structural features such as bulges and internal loops which add complexity to folding dynamics. Importantly, all simulations initiated from fully extended, unfolded configurations, simulating the entire trajectory of folding rather than shortcuts from partially folded states. This rigor provided a stringent test of the model’s predictive power.
The results were remarkably encouraging: 23 out of 26 RNA molecules folded into their experimentally determined native-like conformations. The fidelity of these folds was quantified using root mean square deviation (RMSD) metrics comparing simulation outcomes to known structures. For the simpler stem-loop RNAs, RMSD values were impressively low, under 2 angstroms for the stem regions, and remained below 5 angstroms over the full molecule, signaling high structural accuracy. These findings demonstrate that the integrated DESRES-RNA force field and GB-neck2 solvent approach can reliably replicate the native folding pathways of structurally diverse RNA sequences.
The study also tackled more challenging RNA motifs featuring bulges and internal loops, common in functional RNAs such as ribozymes and riboswitches. Of the eight complex structures studied, five reached their correct fold, an achievement that surpasses previous MD simulation capabilities for such systems. The simulations also unveiled distinct folding pathways unique to these motifs, offering unprecedented insights into the mechanistic routes RNA molecules traverse during folding.
While largely successful, the research highlighted areas needing further refinement. Particularly, the loop regions of the RNA molecules exhibited somewhat less precision with RMSD values nearing 4 angstroms, indicating room for improvement in modeling non-canonical base pairing and the nuanced electrostatic environment. Additionally, the implicit solvent model presently overlooks critical effects of divalent cations like magnesium ions, which substantially stabilize RNA tertiary structures and influence folding kinetics. Optimizing the interaction parameters for these ions and loop dynamics could enhance simulation fidelity further.
The significance of this achievement stretches beyond academic interest. Reliable RNA folding simulations pave the way for rational design of RNA molecules for therapeutic and biotechnological applications. For instance, understanding the folding process aids in developing RNA-targeting drugs capable of combating viral infections such as COVID-19 and influenza, or correcting genetic mutations linked to various diseases and cancers. The ability to predict RNA folding from sequence alone enables predictive screening and optimization without heavy reliance on experimental trial-and-error.
Associate Professor Ando emphasizes the impact of this milestone: “Reproducing the overall folding of basic stem-loop structures with such accuracy marks a new era in the computational exploration of RNA biology. These methods empower scientists to probe not just static structures, but also the dynamic behaviors integral to RNA function. I anticipate expanding applications from molecule design to drug discovery soon.” This study sets a robust computational benchmark, inspiring future innovations that will deepen our molecular understanding and therapeutic targeting of RNA.
The combination of atomistic force fields with efficient implicit solvent models, as demonstrated in this study, offers a promising path forward for molecular simulations. Expanding simulation libraries to include broader RNA classes and refining solvent models will be crucial next steps. Collaborations integrating experimental data and machine learning methodologies could also accelerate improvements, yielding more reliable, scalable simulations to decode the RNA folding code comprehensively.
In summary, through computational ingenuity and rigorous validation, Associate Professor Tadashi Ando’s research marks a transformative leap in modeling RNA folding. The ability to simulate complex RNA stem loops accurately from unfolded states unlocks the potential for high-resolution mechanistic understanding and innovative RNA-based therapeutics, heralding a new chapter in molecular biology and biomedicine.
Subject of Research:
Not applicable
Article Title:
Molecular Dynamics Simulations of RNA Stem-Loop Folding Using an Atomistic Force Field and a Generalized Born Implicit Solvent
News Publication Date:
26-Oct-2025
Web References:
https://pubs.acs.org/doi/10.1021/acsomega.5c05377
References:
DOI: 10.1021/acsomega.5c05377
Image Credits:
Associate Professor Tadashi Ando, Tokyo University of Science, Japan
Keywords:
Bioengineering, Biotechnology, Genetic material, RNA, Life sciences, Drug development, Drug design
