TBI Server: A Web Server for Predicting Ion Effects in RNA Folding

Zhu, Yuhong; He, Zhaojian; Chen, Shi‐Jie

doi:10.1371/journal.pone.0119705

Cited by 8 publications

(9 citation statements)

References 36 publications

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“…An alternative approach is to predict the binding of water molecules and bound metal ions to the RNA prior to docking [253][254][255][256][257][258][259][260] then treat the predicted bound water molecules and/or ions as part of the receptor for RNA-small molecule docking. The Tightly Binding Ion (TBI) 256,261,262 model and the Monte Carlo TBI (MCTBI) model 260,263 predict the ion distribution around an RNA structure. Through explicit sampling of the discrete ion distributions, TBI and MCTBI go beyond the mean-field Poisson-Boltzmann theory by accounting for the correlation between the different ions.…”

Section: Accounting For Solvent-mediated Interactionsmentioning

confidence: 99%

RNA–ligand molecular docking: Advances and challenges

Zhou

Jiang

Chen

2021

WIREs Comput Mol Sci

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With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

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Section: Accounting For Solvent-mediated Interactionsmentioning

confidence: 99%

RNA–ligand molecular docking: Advances and challenges

Zhou

Jiang

Chen

2021

WIREs Comput Mol Sci

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“…• The role played by the environmental conditions such as ions that strongly influence the RNA structure has to be fully investigated and clarified [58,59,60]. Since in vivo RNA can adapt different conformations with respect to in vitro ones, this will be also important to understand such differences and give important information for RNA biology.…”

Section: Future Challenges and Outlookmentioning

confidence: 99%

Shedding light on the dark matter of the biomolecular structural universe: Progress in RNA 3D structure prediction

Pucci

Schug

2019

Methods

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Structured RNA plays many functionally relevant roles in molecular life. Structural information, while required to understand the functional cycles in detail, is challenging to gather. Computational methods promise to complement experimental efforts by predicting three-dimensional RNA models. Here, we provide a concise view of the state of the art methodologies with a focus on the strengths and the weaknesses of the different approaches. Furthermore, we analyzed the recent developments regarding the use of coevolutionary information and how it can boost the prediction performances. We finally discuss some open perspectives and challenges for the near future in the RNA structural stability field.

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“…Therefore, the boundary of the TB region, the available ion binding modes, and the electrostatic energy for each ion binding model are all dependent on the crowder distribution C . For a given crowder distribution C and ion binding mode M , the electrostatic energy for the electric charges in the TB region can be computed as the sum of the self-energy, the polarization energy, and the Coulomb energy of the charges: 41–43 …”

Section: Crowding Model For An Ion-crowder-rna Systemmentioning

confidence: 99%

“…41–43 Ensemble average over all the different modes M gives the mean electrostatic free energy

Δ G_{ele}^{(C)}

for a given crowder distribution C :

Δ G_{ele}^{(C)} = {〈 Δ G_{TB}^{(M)} + Δ G_{DB}^{(M)} 〉}_{M}

…”

Section: Crowding Model For An Ion-crowder-rna Systemmentioning

confidence: 99%

“…several molar concentration) and can thus become strongly correlated. To account for the ion correlation and fluctuation effects, we use the previously developed TBI model. − The basic idea of the TBI model is to consider correlated ion distributions by enumerating discrete, many-body ion distributions. Mathematically, this is achieved by distinguishing two different regions: the tightly bound (TB) and the diffusely bound (DB) regions, corresponding to the regions of ions with strong and weak Coulomb correlations, respectively.…”

Section: Crowding Model For An Ion–crowder–rna Systemmentioning

confidence: 99%

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Predicting Molecular Crowding Effects in Ion–RNA Interactions

Zhu

et al. 2016

J. Phys. Chem. B

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We develop a new statistical mechanical model to predict the molecular crowding effects in ion-RNA interactions. By considering discrete distributions of the crowders, the model can treat the main crowder-induced effects, such as the competition with ions for RNA binding, changes of the electrostatic interaction due to crowder-induced changes in the dielectric environment, and changes in the nonpolar hydration state of the crowder-RNA system. To enhance the computational efficiency, we sample the crowder distribution using a hybrid approach: for crowders in the close vicinity of RNA surface, we sample their discrete distributions; for crowders in the bulk solvent away from the RNA surface, we use a continuous mean-field distribution for the crowders. Moreover, using the Tightly Bound Ion (TBI) model, the model accounts for ion fluctuation and correlation effects in the calculation for ion-RNA interactions. Applications of the model to a variety of simple RNA structures such as RNA helices show a crowder-induced increase in free energy and decrease in ion binding. Such crowding effects tend to contribute to the destabilization of RNA structure. Further analysis indicates that these effects are associated with the crowder-ion competition in RNA binding and the effective decrease in the dielectric constant. This simple ion effect model may serve as a useful framework for modeling more realistic crowders with larger, more complex RNA structures.

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TBI Server: A Web Server for Predicting Ion Effects in RNA Folding

Cited by 8 publications

References 36 publications

RNA–ligand molecular docking: Advances and challenges

RNA–ligand molecular docking: Advances and challenges

Shedding light on the dark matter of the biomolecular structural universe: Progress in RNA 3D structure prediction

Predicting Molecular Crowding Effects in Ion–RNA Interactions

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