GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization

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.

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RNA-align: quick and accurate alignment of RNA 3D structures based on size-independent TM-scoreRNA

Bioinformatics ◽

10.1093/bioinformatics/btz282 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4459-4461 ◽

Cited By ~ 1

Author(s):

Sha Gong ◽

Chengxin Zhang ◽

Yang Zhang

Keyword(s):

Rna Structure ◽

Large Scale ◽

3D Structure ◽

Structure Alignment ◽

Coarse Grained ◽

Supplementary Information ◽

Art Programs ◽

3D Structures ◽

Rna Molecules ◽

Functional Relations

Abstract Motivation Comparison of RNA 3D structures can be used to infer functional relationship of RNA molecules. Most of the current RNA structure alignment programs are built on size-dependent scales, which complicate the interpretation of structure and functional relations. Meanwhile, the low speed prevents the programs from being applied to large-scale RNA structural database search. Results We developed an open-source algorithm, RNA-align, for RNA 3D structure alignment which has the structure similarity scaled by a size-independent and statistically interpretable scoring metric. Large-scale benchmark tests show that RNA-align significantly outperforms other state-of-the-art programs in both alignment accuracy and running speed. The major advantage of RNA-align lies at the quick convergence of the heuristic alignment iterations and the coarse-grained secondary structure assignment, both of which are crucial to the speed and accuracy of RNA structure alignments. Availability and implementation https://zhanglab.ccmb.med.umich.edu/RNA-align/. Supplementary information Supplementary data are available at Bioinformatics online.

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RNA 3D Structure Prediction Using Coarse-Grained Models

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.720937 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jun Li ◽

Shi-Jie Chen

Keyword(s):

Structure Prediction ◽

3D Structure ◽

Three Dimensional ◽

Coarse Grained ◽

Biological Functions ◽

3D Structures ◽

Rna Molecules ◽

Atomic Structures ◽

Long Time

The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.

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Structural 3D Domain Reconstruction of the RNA Genome from Viruses with Secondary Structure Models

Viruses ◽

10.3390/v13081555 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1555

Author(s):

Simón Poblete ◽

Horacio V. Guzman

Keyword(s):

Secondary Structure ◽

High Reliability ◽

3D Structure ◽

Three Dimensional ◽

Coarse Grained ◽

Entire Genome ◽

Rna Molecules ◽

Secondary Structure Models ◽

Rna Genome ◽

Self Association

Three-dimensional RNA domain reconstruction is important for the assembly, disassembly and delivery functionalities of a packed proteinaceus capsid. However, to date, the self-association of RNA molecules is still an open problem. Recent chemical probing reports provide, with high reliability, the secondary structure of diverse RNA ensembles, such as those of viral genomes. Here, we present a method for reconstructing the complete 3D structure of RNA genomes, which combines a coarse-grained model with a subdomain composition scheme to obtain the entire genome inside proteinaceus capsids based on secondary structures from experimental techniques. Despite the amount of sampling involved in the folded and also unfolded RNA molecules, advanced microscope techniques can provide points of anchoring, which enhance our model to include interactions between capsid pentamers and RNA subdomains. To test our method, we tackle the satellite tobacco mosaic virus (STMV) genome, which has been widely studied by both experimental and computational communities. We provide not only a methodology to structurally analyze the tertiary conformations of the RNA genome inside capsids, but a flexible platform that allows the easy implementation of features/descriptors coming from both theoretical and experimental approaches.

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RNA Structure Analysis: A Multifaceted Approach

Pattern Discovery in Biomolecular Data ◽

10.1093/oso/9780195119404.003.0018 ◽

1999 ◽

Author(s):

Bruce A. Shapiro ◽

Wojciech Kasprzak

Keyword(s):

Nucleic Acid ◽

Secondary Structure ◽

Tertiary Structure ◽

Hammerhead Ribozyme ◽

3D Structure ◽

Computer Prediction ◽

Rna Molecules ◽

Tertiary Interactions ◽

The One ◽

Secondary And Tertiary Structure

Genomic information (nucleic acid and amino acid sequences) completely determines the characteristics of the nucleic acid and protein molecules that express a living organism’s function. One of the greatest challenges in which computation is playing a role is the prediction of higher order structure from the one-dimensional sequence of genes. Rules for determining macromolecule folding have been continually evolving. Specifically in the case of RNA (ribonucleic acid) there are rules and computer algorithms/systems (see below) that partially predict and can help analyze the secondary and tertiary interactions of distant parts of the polymer chain. These successes are very important for determining the structural and functional characteristics of RNA in disease processes and hi the cell life cycle. It has been shown that molecules with the same function have the potential to fold into similar structures though they might differ in their primary sequences. This fact also illustrates the importance of secondary and tertiary structure in relation to function. Examples of such constancy in secondary structure exist in transfer RNAs (tRNAs), 5s RNAs, 16s RNAs, viroid RNAs, and portions of retroviruses such as HIV. The secondary and tertiary structure of tRNA Phe (Kim et al., 1974), of a hammerhead ribozyme (Pley et al., 1994), and of Tetrahymena (Cate et al., 1996a, 1996b) have been shown by their crystal structure. Currently little is known of tertiary interactions, but studies on tRNA indicate these are weaker than secondary structure interactions (Riesner and Romer, 1973; Crothers and Cole, 1978; Jaeger et al., 1989b). It is very difficult to crystallize and/or get nuclear magnetic resonance spectrum data for large RNA molecules. Therefore, a logical place to start in determining the 3D structure of RNA is computer prediction of the secondary structure. The sequence (primary structure) of an RNA molecule is relatively easy to produce. Because experimental methods for determining RNA secondary and tertiary structure (when the primary sequence folds back on itself and forms base pairs) have not kept pace with the rapid discovery of RNA molecules and their function, use of and methods for computer prediction of secondary and tertiary structures have increasingly been developed.

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Coarse-grained dynamic RNA titration simulations

Interface Focus ◽

10.1098/rsfs.2018.0066 ◽

2019 ◽

Vol 9 (3) ◽

pp. 20180066 ◽

Cited By ~ 2

Author(s):

S. Pasquali ◽

E. Frezza ◽

F. L. Barroso da Silva

Keyword(s):

Electrostatic Interactions ◽

Molecular Organization ◽

Coarse Grained ◽

Solution Ph ◽

Dynamic Simulations ◽

Rna Molecules ◽

Coarse Grained Model ◽

Constant Ph ◽

Conformational Plasticity ◽

And Function

Electrostatic interactions play a pivotal role in many biomolecular processes. The molecular organization and function in biological systems are largely determined by these interactions. Owing to the highly negative charge of RNA, the effect is expected to be more pronounced in this system. Moreover, RNA base pairing is dependent on the charge of the base, giving rise to alternative secondary and tertiary structures. The equilibrium between uncharged and charged bases is regulated by the solution pH, which is therefore a key environmental condition influencing the molecule’s structure and behaviour. By means of constant-pH Monte Carlo simulations based on a fast proton titration scheme, coupled with the coarse-grained model HiRE-RNA, molecular dynamic simulations of RNA molecules at constant pH enable us to explore the RNA conformational plasticity at different pH values as well as to compute electrostatic properties as local p K a values for each nucleotide.

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Coarse-grained modeling of RNA 3D structure

Methods ◽

10.1016/j.ymeth.2016.04.026 ◽

2016 ◽

Vol 103 ◽

pp. 138-156 ◽

Cited By ~ 25

Author(s):

Wayne K. Dawson ◽

Maciej Maciejczyk ◽

Elzbieta J. Jankowska ◽

Janusz M. Bujnicki

Keyword(s):

3D Structure ◽

Coarse Grained

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Detailed Analysis of 17β-Estradiol-Aptamer InteractionsAptamer Interactions: A Molecular Dynamics Simulation Study

10.20944/preprints201806.0023.v2 ◽

2018 ◽

Author(s):

Alexander Eisold ◽

Dirk Labudde

Keyword(s):

Md Simulation ◽

3D Structure ◽

Dynamics Simulation ◽

Coarse Grained ◽

Negative Effects ◽

Time Step ◽

Binding Characteristics ◽

Structure Information ◽

Potential Binding ◽

Interior Loop

Micro-pollutants such as 17β-Estradiol (E2) have been detected in different water resources and their negative effects on the environment and organisms have been observed. Aptamers are established as a possible detection tool, but the underlying ligand binding is largely unexplored. In this study, a previously described 35-mer E2-specific aptamer was used to analyse the binding characteristics between E2 and the aptamer with a MD simulation in an aqueous medium. Because there is no 3D structure information available for this aptamer, it was modeled using coarse-grained modeling method. The E2 ligand was positioned inside a potential binding area of the predicted aptamer structure, the complex was used for an 25 ns MD simulation, and the interactions were examined for each time step. We identified E2-specific bases within the interior loop of the aptamer and also demonstrated the influence of frequently underestimated water-mediated hydrogen bonds. The study contributes to the understanding of the behavior of ligands binding with aptamer structure in an aqueous solution. The developed workflow allows generating and examining further appealing ligand-aptamer complexes.

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Theory and simulations for RNA folding in mixtures of monovalent and divalent cations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1911632116 ◽

2019 ◽

Vol 116 (42) ◽

pp. 21022-21030 ◽

Cited By ~ 12

Author(s):

Hung T. Nguyen ◽

Naoto Hori ◽

D. Thirumalai

Keyword(s):

Rna Folding ◽

Divalent Cations ◽

Coarse Grained ◽

Concentration Difference ◽

Rna Molecules ◽

Site Model ◽

Monovalent Ions ◽

Wide Range ◽

Sequence Effects ◽

The Impact

RNA molecules cannot fold in the absence of counterions. Experiments are typically performed in the presence of monovalent and divalent cations. How to treat the impact of a solution containing a mixture of both ion types on RNA folding has remained a challenging problem for decades. By exploiting the large concentration difference between divalent and monovalent ions used in experiments, we develop a theory based on the reference interaction site model (RISM), which allows us to treat divalent cations explicitly while keeping the implicit screening effect due to monovalent ions. Our theory captures both the inner shell and outer shell coordination of divalent cations to phosphate groups, which we demonstrate is crucial for an accurate calculation of RNA folding thermodynamics. The RISM theory for ion–phosphate interactions when combined with simulations based on a transferable coarse-grained model allows us to predict accurately the folding of several RNA molecules in a mixture containing monovalent and divalent ions. The calculated folding free energies and ion-preferential coefficients for RNA molecules (pseudoknots, a fragment of the rRNA, and the aptamer domain of the adenine riboswitch) are in excellent agreement with experiments over a wide range of monovalent and divalent ion concentrations. Because the theory is general, it can be readily used to investigate ion and sequence effects on DNA properties.

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