Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions

Wang, Xunxun; Tan, Ya-Lan; Yu, Shixiong; Shi, Yuexi; Tan, Zhi-Jie

doi:10.1016/j.bpj.2023.03.017

Cited by 7 publications

(19 citation statements)

References 96 publications

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“…Generally, a physics-based CG model guides RNA structure folding through a specific force field (energy function) and a conformation sampling algorithm such as Monte Carlo (MC) [82] or molecular dynamics (MD) sampling [83,84]. A CG force field in these models is generally composed of bonded and non-bonded energy terms [30][31][32][33][34][35].…”

Section: Physics-based Modelsmentioning

confidence: 99%

“…Here, RNA-Puzzles is a CASP-like competition for RNA 3D structure prediction [19][20][21][22][23], and CASP-RNA is a prediction competition for RNA 3D structures newly present in CASP15 [24]. Depending on the methods for generating the 3D structure ensemble, the existing computational models can be roughly divided into physics-based [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43], knowledge-based fragment assembly [44][45][46][47][48][49][50][51][52][53][54][55][56], and deep-learning-based ones [57][58][59][60]; see Figure 1. Second, a structure evaluation is required to identify the top near-native structures from the precedingly predicted 3D structure candidate ensemble [61,62], and a high-quality scoring function/energy function for structure evalua...…”

Section: Introductionmentioning

confidence: 99%

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RNA 3D Structure Prediction: Progress and Perspective

Wang

Lou

et al. 2023

Molecules

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Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

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Section: Physics-based Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RNA 3D Structure Prediction: Progress and Perspective

Wang

Lou

et al. 2023

Molecules

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“…Multiple unique stable conformations for the SARS-CoV-2 frameshift pseudoknot have been predicted and observed via various methods including crystallography, cryo-EM, and 3D-physics simulations [ 5 , 12 , 35 , 37 – 39 ]. Thermal unfolding of RNA found major and minor paths from the folded to unfolded state, concluding that stability of transient states dictates folding paths [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot

Trinity,

Stege,

Jabbari

2024

PLoS Comput Biol

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Understanding and targeting functional RNA structures towards treatment of coronavirus infection can help us to prepare for novel variants of SARS-CoV-2 (the virus causing COVID-19), and any other coronaviruses that could emerge via human-to-human transmission or potential zoonotic (inter-species) events. Leveraging the fact that all coronaviruses use a mechanism known as −1 programmed ribosomal frameshifting (−1 PRF) to replicate, we apply algorithms to predict the most energetically favourable secondary structures (each nucleotide involved in at most one pairing) that may be involved in regulating the −1 PRF event in coronaviruses, especially SARS-CoV-2. We compute previously unknown most stable structure predictions for the frameshift site of coronaviruses via hierarchical folding, a biologically motivated framework where initial non-crossing structure folds first, followed by subsequent, possibly crossing (pseudoknotted), structures. Using mutual information from 181 coronavirus sequences, in conjunction with the algorithm KnotAli, we compute secondary structure predictions for the frameshift site of different coronaviruses. We then utilize the Shapify algorithm to obtain most stable SARS-CoV-2 secondary structure predictions guided by frameshift sequence-specific and genome-wide experimental data. We build on our previous secondary structure investigation of the singular SARS-CoV-2 68 nt frameshift element sequence, by using Shapify to obtain predictions for 132 extended sequences and including covariation information. Previous investigations have not applied hierarchical folding to extended length SARS-CoV-2 frameshift sequences. By doing so, we simulate the effects of ribosome interaction with the frameshift site, providing insight to biological function. We contribute in-depth discussion to contextualize secondary structure dual-graph motifs for SARS-CoV-2, highlighting the energetic stability of the previously identified 3_8 motif alongside the known dominant 3_3 and 3_6 (native-type) −1 PRF structures. Using a combination of thermodynamic methods and sequence covariation, our novel predictions suggest function of the attenuator hairpin via previously unknown pseudoknotted base pairing. While certain initial RNA folding is consistent, other pseudoknotted base pairs form which indicate potential conformational switching between the two structures.

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“…For example, we have developed a three-bead CG model (using atoms of P, C4′, and N1 for pyrimidine or N9 for purine to represent each nucleotide) for RNA folding. Combining the sequence/salt-dependent CG potentials with Monte Carlo (MC)-simulated annealing or a replica exchange MC algorithm, the model can predict 3D structures and thermodynamic stability for RNA hairpins, duplexes, kissing complexes, and pseudoknots in monovalent/divalent ion solutions from sequences [ 29 , 30 , 31 , 32 , 33 , 34 ]. However, while the predicted CG structures from most of the CG models capture the primary topological information of RNA molecules, they are limited for practical applications due to the lack of atomistic details.…”

Section: Introductionmentioning

confidence: 99%

ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures

Shi,

Wu,

et al. 2024

Molecules

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RNAs play crucial roles in various essential biological functions, including catalysis and gene regulation. Despite the widespread use of coarse-grained (CG) models/simulations to study RNA 3D structures and dynamics, their direct application is challenging due to the lack of atomic detail. Therefore, the reconstruction of full atomic structures is desirable. In this study, we introduced a straightforward method called ABC2A for reconstructing all-atom structures from RNA CG models. ABC2A utilizes diverse nucleotide fragments from known structures to assemble full atomic structures based on the CG atoms. The diversification of assembly fragments beyond standard A-form ones, commonly used in other programs, combined with a highly simplified structure refinement process, ensures that ABC2A achieves both high accuracy and rapid speed. Tests on a recent large dataset of 361 RNA experimental structures (30–692 nt) indicate that ABC2A can reconstruct full atomic structures from three-bead CG models with a mean RMSD of ~0.34 Å from experimental structures and an average runtime of ~0.5 s (maximum runtime < 2.5 s). Compared to the state-of-the-art Arena, ABC2A achieves a ~25% improvement in accuracy and is five times faster in speed.

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Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions

Cited by 7 publications

References 96 publications

RNA 3D Structure Prediction: Progress and Perspective

RNA 3D Structure Prediction: Progress and Perspective

Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot

ABC2A: A Straightforward and Fast Method for the Accurate Backmapping of RNA Coarse-Grained Models to All-Atom Structures

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