A New Method to Predict Ion Effects in RNA Folding.

Li-Zhen Sun, Shi-Jie Chen

Open access · greenAbstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

0.9field-weighted citation impact, top 26% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Li-Zhen SunDepartment of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, MO, 65211, USA.

Shi-Jie ChenDepartment of Physics, Department of Biochemistry, and MU Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. chenshi@missouri.edu.

University of Missouri · US

Funding

Statistical Mechanics of RNA FoldingR01GM063732 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI CHEN, SHI-JIE · 2003 to 2018

$3.2M

New computational tools for predicting ion effects in RNA structuresR01GM117059 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI CHEN, SHI-JIE · 2017 to 2020

$1.2M

NIGMS NIH HHS R01 GM063732NIGMS NIH HHS R01 GM117059

6 · The paper itself

Abstract

The strong interaction between metal ions in solution and highly charged RNA molecules is critical for RNA structure formation and stabilization. Metal ions binding to RNA can induce RNA structural changes that are important for RNA cellular functions. Therefore, quantitative modeling of the ion effects is essential for RNA structure prediction and RNA-based molecular design. Recently, inspired by the increasing experimental evidence that supports the importance of ion correlation and fluctuation in ion-RNA interactions, we developed a new computational model, Monte Carlo Tightly Bound Ion (MCTBI) model. The validity of the model is shown by the improved accuracy in the predictions for ion binding properties and ion-dependent free energies for RNA structures. In this chapter, using homodimeric tetraloop-receptor docking as an illustrative example, we showcase the MCTBI method for the computational prediction of the ion effects in RNA folding.

Indexed as

Models, MolecularNucleic Acid ConformationRNA FoldingDatabases, GeneticIonsMonte Carlo MethodRNAStatic ElectricityIonsRNAIon–RNA interactionsMetal ion effectsRNA foldingTightly Bound Ion model

Identifiers

PMID28730429

DOI10.1007/978-1-4939-7138-1_1

PMCPMC5749638

OpenAlexW2738037218

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.