Biology subjects

Shi, Y.-Z.

Publications and source records attributed to Shi, Y.-Z..

5 recordsLinked to original sources

bioRxiv2026

Spatial confinement reshapes the folding of an ion-stabilized DNA with three-way junction

Within the densely packed cellular milieu, the structural dynamics and stability of complex DNA architectures are governed by an intricate interplay between spatial confinement and ionic environment. Here we employ our coarse-grained model, DNAfold2, to dissect the mechanistic principles underlying how nanoscale confinement reshapes the folding landscape of a DNA with three-way junction across a broad range of ionic conditions. Our simulations demonstrate that spatial confinement acts as a structural selector, preferentially stabilizing compact, well-defined junction topologies while significantly reducing conformational flexibility in response to ionic strength variations. Through rigorous analysis of structural state populations, we reveal that this behavior results from the entropic exclusion of extended intermediates and unfolded states, effectively reshaping the DNA stability under spatial confinement. Notably, spatial confinement induces a fundamental reshape of the unfolding pathway, suppressing heterogeneous intermediate ensembles and enforcing a highly cooperative transition. These findings establish a unified mechanistic framework for understanding the biophysics of nucleic acid in physiologically crowded environments, with significant implications for de novo design of biomolecular nanomaterials and mechanistic regulation of DNA-processing machinery in vivo.

biophysics↗

bioRxiv2025

gCoSRNA: Generalizable Coaxial-Stacking Prediction for RNA Junctions Using Secondary Structure

Coaxial stacking between adjacent stems is a key tertiary interaction that defines the spatial organization of RNA junctions, which are core structural motifs in folded RNAs. Accurate prediction of coaxial stacking is critical for RNA 3D structure modeling, yet existing computational tools remain limited, especially for junctions with variable numbers of branches or complex topologies. Here, we present gCoSRNA, a generalizable computational framework for predicting coaxial stacking configurations using RNA sequence and secondary structure as input. Instead of developing separate models for each junction type, gCoSRNA decomposes multi-way junctions into all possible adjacent stem pairs, termed pseudo two-way junctions, and uses a unified random forest classifier to evaluate stacking probabilities. Global stacking configurations are inferred by integrating these pairwise predictions, eliminating the need for explicit junction-type classification. Benchmarking on two independent test sets, including CASP15/16 and RNA-Puzzles targets, shows that gCoSRNA achieves consistently high accuracy (mean [~]0.87) across junctions with two to seven branches, outperforming existing junction-specific methods. These results highlight the models ability to capture higher-order structural features and its potential utility in RNA tertiary structure prediction pipelines. The source code is freely available at https://github.com/RNA-folding-lab/gCoSRNA.

bioinformatics↗

bioRxiv2022

Ab initio predictions for 3D structure and stability of single- and double-stranded DNAs in ion solutions

The three-dimensional (3D) structure and stability of DNA are essential to understand/control their biological functions and aid the development of novel materials. In this work, we present a coarse-grained (CG) model for DNA based on the RNA CG model proposed by us, to predict 3D structures and stability for both dsDNA and ssDNA from the sequence. Combined with a Monte Carlo simulated annealing algorithm and CG force fields involving the sequence-dependent base-pairing/stacking interactions and an implicit electrostatic potential, the present model successfully folds 20 dsDNAs ([≤]52nt) and 20 ssDNAs ([≤]74nt) into the corresponding native-like structures just from their sequences, with an overall mean RMSD of 3.4[A] from the experimental structures. For DNAs with various lengths and sequences, the present model can make reliable predictions on stability, e.g., for 27 dsDNAs with/without bulge/internal loops and 24 ssDNAs including pseudoknot, the mean deviation of predicted melting temperatures from the corresponding experimental data is only ~2.0. Furthermore, the model also quantificationally predicts the effects of monovalent or divalent ions on the structure stability of ssDNAs/dsDNAs. Author SummaryTo determine 3D structures and quantify stability of single- (ss) and double-stranded (ds) DNAs is essential to unveil the mechanisms of their functions and to further guide the production and development of novel materials. Although many DNA models have been proposed to reproduce the basic structural, mechanical, or thermodynamic properties of dsDNAs based on the secondary structure information or preset constraints, there are very few models can be used to investigate the ssDNA folding or dsDNA assembly from the sequence. Furthermore, due to the polyanionic nature of DNAs, metal ions (e.g., Na+ and Mg2+) in solutions can play an essential role in DNA folding and dynamics. Nevertheless, ab initio predictions for DNA folding in ion solutions are still an unresolved problem. In this work, we developed a novel coarse-grained model to predict 3D structures and thermodynamic stabilities for both ssDNAs and dsDNAs in monovalent/divalent ion solutions from their sequences. As compared with the extensive experimental data and available existing models, we showed that the present model can successfully fold simple DNAs into their native-like structures, and can also accurately reproduce the effects of sequence and monovalent/divalent ions on structure stability for ssDNAs including pseudoknot and dsDNAs with/without bulge/internal loops.

biophysics↗

bioRxiv2021

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that, rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. Additionally, rsRNASP is also superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available at website https://github.com/Tan-group/rsRNASP. SIGNIFICANCERNAs play crucial roles in catalyzing biochemical reactions and regulating gene expression, and the biological functions of RNAs are generally coupled to their structures. Complementary to experiments, developing computational models to predict RNA 3D structures can be very helpful for understanding RNA biology functions. For a computational model, a reliable energy function is essentially important either for guiding conformational folding or for structure evaluation. For this purpose, we developed a residue-separation-based distance-dependent statistical potential, named rsRNASP which distinguishes the short- and long-ranged interactions, for RNA 3D structure evaluation. Our rsRNASP were examined against extensive test sets and shows overall superior performance over existing top traditional statistical potentials and a recently developed scoring function through 3D convolutional neural networks, especially for realistic test set from various computational structure prediction models.

bioinformatics↗

bioRxiv2018

Coarse-grained modeling for structure, stability and flexibility of double stranded RNAs in salt solutions

Double-stranded (ds) RNAs play essential roles in many processes of cell metabolism. The knowledge of three-dimensional (3D) structure, stability and flexibility of dsRNAs in salt solutions is important for understanding their biological functions. In this work, we further developed our previously proposed coarse-grained model to predict 3D structure, stability and flexibility for dsRNAs in monovalent and divalent ion solutions through involving an implicit structure-based electrostatic potential. The model can make reliable predictions for 3D structures of extensive dsRNAs with/without bulge/internal loops from their sequences, and the involvement of the structure-based electrostatic potential and corresponding ion condition can improve the predictions on 3D structures of dsRNAs in ion solutions. Furthermore, the model can make good predictions on thermal stability for extensive dsRNAs over the wide range of monovalent/divalent ion concentrations, and our analyses show that thermally unfolding pathway of a dsRNA is generally dependent on its length as well as its sequence. In addition, the model was employed to examine the salt-dependent flexibility of a dsRNA helix and the calculated salt-dependent persistence lengths are in good accordance with experiments.

biophysics↗