To what extent of ion neutralization can multivalent ion distributions around RNA-like macroions be described by Poisson–Boltzmann theory?

Xiong, Gui; Kun, Xi; Zhang, Xi; Tan, Zhi-Jie

doi:10.1088/1674-1056/27/1/018203

Cited by 4 publications

(4 citation statements)

References 87 publications

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“…The separate involvement of secondary and tertiary contributions in a statistical potential may improve its performance. Furthermore, basestacking can promote the helical orientation of RNAs and consequently RNA structures are apparently less spherical than protein structures, [105,106] which may suggest that angular or orientation-dependent contribution is more important for a statistical potential for RNAs.…”

Section: On Unique Feature Of Rnasmentioning

confidence: 99%

“…Very importantly, RNAs are strongly charged polyanion, [106][107][108][109] which is distinctively different from proteins generally with certain positively and negatively charged domains. Generally, for RNAs, metal ions are essentially required to neutralize the negative charges on RNA backbones, and consequently would favor the folding of compact native structures.…”

Section: On Unique Feature Of Rnasmentioning

confidence: 99%

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Statistical potentials for 3D structure evaluation: From proteins to RNAs*

et al. 2021

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Structure evaluation is critical to in silico 3-dimensional structure predictions for biomacromolecules such as proteins and RNAs. For proteins, structure evaluation has been paid attention over three decades along with protein folding problem, and statistical potentials have been shown to be effective and efficient in protein structure prediction and evaluation. In recent two decades, RNA folding problem has attracted much attention and several statistical potentials have been developed for RNA structure evaluation, partially with the aid of the progress in protein structure prediction. In this review, we will firstly give a brief overview on the existing statistical potentials for protein structure evaluation. Afterwards, we will introduce the recently developed statistical potentials for RNA structure evaluation. Finally, we will emphasize the perspective on developing new statistical potentials for RNAs in the near future.

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Section: On Unique Feature Of Rnasmentioning

confidence: 99%

Section: On Unique Feature Of Rnasmentioning

confidence: 99%

Statistical potentials for 3D structure evaluation: From proteins to RNAs*

et al. 2021

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“…However, the CC theory only works well at very dilute ion concentrations while not at finite concentrations, [41] and the PB theory ignores the correlation between ions and significantly underestimates the effect of multivalent ions in stabilizing RNAs. [42,43] Recently, the tightly bound ion (TBI) model has been developed by accounting for fluctuations and ion-ion correlations. [44,45] The TBI model separates the tightly bound ions from the diffusive ions in solution, and explicitly accounts for the correlation between the tightly bound ions and the discrete binding modes.…”

Section: Electrostatic Interactions In Rna Structuresmentioning

confidence: 99%

Computational prediction of RNA tertiary structures using machine learning methods*

Huang¹,

Du²,

Zhang³

et al. 2020

Chinese Phys. B

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RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

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“…Proteins are involved in nearly all biological processes in the cell such as DNA replication and repair, RNA transcription, signaling pathways, immune system response, and cell regulation. [1][2][3][4][5][6][7] In order to function, almost all of proteins interact with other proteins to form complexes or proteinprotein interaction networks. [8,9] Therefore, determining the complex structure of the proteins involved is a crucial step in understanding their interaction mechanism and thus for the development of therapeutic interventions targeting the interactions.…”

Section: Introductionmentioning

confidence: 99%

Protein–protein docking with interface residue restraints*

Huang

2021

Chinese Phys. B

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The prediction of protein–protein complex structures is crucial for fundamental understanding of celluar processes and drug design. Despite significant progresses in the field, the accuracy of ab initio docking without using any experimental restraints remains relatively low. With the rapid advancement of structural biology, more and more information about binding can be derived from experimental data such as NMR experiments or chemical cross-linking. In addition, information about the residue contacts between proteins may also be derived from their sequences by using evolutionary analysis or deep learning. Here, we propose an efficient approach to incorporate interface residue restraints into protein–protein docking, which is named as HDOCKsite. Extensive evaluations on the protein–protein docking benchmark 4.0 showed that HDOCKsite significantly improved the docking performance and obtained a much higher success rate in binding mode predictions than original ab initio docking.

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To what extent of ion neutralization can multivalent ion distributions around RNA-like macroions be described by Poisson–Boltzmann theory?

Cited by 4 publications

References 87 publications

Statistical potentials for 3D structure evaluation: From proteins to RNAs*

Statistical potentials for 3D structure evaluation: From proteins to RNAs*

Computational prediction of RNA tertiary structures using machine learning methods*

Protein–protein docking with interface residue restraints*

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