scholarly journals A coarse-grained model with implicit salt for RNAs: Predicting 3D structure, stability and salt effect

2014 ◽  
Vol 141 (10) ◽  
pp. 105102 ◽  
Author(s):  
Ya-Zhou Shi ◽  
Feng-Hua Wang ◽  
Yuan-Yan Wu ◽  
Zhi-Jie Tan
Keyword(s):  
Salt Effect ◽  
3D Structure ◽  
Coarse Grained ◽  
Structure Stability ◽  
Coarse Grained Model

3D structure stability of the HIV-1 TAR RNA in ion solutions: A coarse-grained model study

2019 ◽  
Vol 151 (16) ◽  
pp. 165101
Author(s):  
Ben-Gong Zhang ◽  
Hua-Hai Qiu ◽  
Jian Jiang ◽  
Jie Liu ◽  
Ya-Zhou Shi
Keyword(s):  
3D Structure ◽  
Coarse Grained ◽  
Structure Stability ◽  
Coarse Grained Model ◽  
Model Study ◽  
Tar Rna ◽  
Hiv 1

scholarly journals Modeling structure, stability and flexibility of double-stranded RNAs in salt solutions

2018 ◽  
Author(s):  
L. Jin ◽  
Y.Z. Shi ◽  
C.J. Feng ◽  
Y.L. Tan ◽  
Z.J. Tan
Keyword(s):  
Electrostatic Potential ◽  
Cell Metabolism ◽  
3D Structure ◽  
Three Dimensional ◽  
Coarse Grained ◽  
Structure Stability ◽  
Salt Solutions ◽  
3D Structures ◽  
Coarse Grained Model ◽  
Wide Range

AbstractDouble-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.

scholarly journals Theoretical and computational modeling of naturally and artificially modified RNA nucleotides

2021 ◽  
Author(s):  
◽  
Travis Caleb Hurst
Keyword(s):  
3D Structure ◽  
Experimental Information ◽  
Potential Of Mean Force ◽  
Free Energy Calculations ◽  
Coarse Grained ◽  
Structure Stability ◽  
Rna Molecules ◽  
Parameterization Method ◽  
The Impact ◽  
Structural Ensemble

Ribonucleic acid (RNA) is a polymeric nucleic acid that is crucial for cellular function, regulating gene expression and encoding/decoding protein/DNA molecules. Recent discoveries of diverse functionality in non-coding RNAs have led to unprecedented demand for RNA 3D structure determination. With current technology, general, accurate prediction of 3D structures for large RNAs from the sequence remains computationally intractable. One of the principal challenges arises from the conformational flexibility of RNA, especially in loop/junction regions, which results in a rugged energy landscape. Several strategies exist to overcome this challenge, including incorporation of efficient experimental information and coarse-grained (CG) modeling to improve computational sampling of the structural ensemble. A second challenge is the inclusion of naturally modified derivatives of canonical RNA nucleotides in structure analysis. Most RNA prediction strategies rely upon the canonical nucleotides (adenine (A), uracil (U), guanine (G), and cytosine (C)), ignoring the effects of modified nucleotides on the structure and system dynamics. In general, RNA molecules contain rigid and flexible structural elements, which can be probed using efficient selective 2'-hydroxyl analyzed by primer extension (SHAPE) experiments. SHAPE experiments selectively modify flexible RNA nucleotides and can be processed to produce a characteristic reactivity profile for an RNA molecule that contains structural information. Incorporation of efficient experimental information, such as SHAPE, in predicting RNA 3D structure is highly desirable for overcoming the current knowledge gap between RNA sequence and 3D structure. In the first project, we introduce a physics-based model, the 3D structure-SHAPE relationship (3DSSR) model, to predict the SHAPE reactivity from the structure and show how this model may be used to sieve SHAPE-compatible structures from a pool of low-energy decoys and refine our predictions. In the second project, we compare 3DSSR performance to that of a convolutional neural network (CNN) trained on the SHAPE data and RNA structures, showing that 3DSSR outperforms the CNN given the limited data available. In the third project, we further improve the 3DSSR model, gaining deeper insights into the SHAPE reaction and biases. In the fourth project, we explore the theory underpinning the iterative simulated CG RNA folding model (IsRNA). In establishing the underlying mechanics driving the success of the model, we were able to clarify and improve the parameterization method while expanding the model interpretation, which should broaden application of the method to other biopolymers, such as protein. We found that the parameterization method follows statistical mechanics principles but also has a Bayesian interpretation. Further, we found that the parameterization process can benefit from application of the principle of maximum entropy, which improves simulation and parameterization efficiency. In the fifth project, we investigate the impact of nucleotide modification on the structure and configurational ensemble of RNA molecules using free energy calculations. By applying modifications to a common RNA hairpin, we estimate the impact on the stability of the structural ensemble, identifying specific interactions that drive changes to the potential of mean force (PMF) and showing the context and modification-dependence of the variable alterations to the structure stability.

scholarly journals One-Bead Coarse-Grained Model for RNA 3D Structure with Non-Canonical Interactions

2019 ◽  
Vol 116 (3) ◽  
pp. 353a
Author(s):  
Mario Villada-Balbuena ◽  
Mauricio D. Carbajal-Tinoco
Keyword(s):  
3D Structure ◽  
Coarse Grained ◽  
Coarse Grained Model

scholarly journals Chromosome compartmentalization replacement during stem cell differentiation

2018 ◽  
Author(s):  
Y.A. Eidelman ◽  
S.V. Slanina ◽  
V.S. Pyatenko ◽  
S.G. Andreev
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Large Scale ◽  
3D Structure ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Coarse Grained ◽  
Interphase Chromosome ◽  
Coarse Grained Model ◽  
Differentiated Cells

ABSTRACTIn this paper, changes in a large-scale 3D structure of chromosomes during stem cell differentiation is studied. The polymer coarse-grained model of a human interphase chromosome is introduced which reproduces the experimental Hi-C contact maps in chromosomes 12, 17 for both embryonic stem and differentiated cells with high accuracy. Model based analysis of Hi-C data suggests a mechanism of establishment of preferential long-range chromosomal contacts and compartmentalization replacement during cell stem differentiation. The model provides the conceptual basis for integration of data on the dynamics of chromatin interactions, the 3D structure of chromosomes and gene expression during stem cell differentiation or reprogramming.

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