Evidence map›Paper›PMID 34058714›Full record

ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2021

Theoretical classification of exchange geometries from the perspective of NMR relaxation dispersion.

Fa-An Chao, Yue Zhang, R Andrew Byrd

Abstract read
In one paragraph

Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. The A39G FF domain folds on a volcano-shaped free energy surface via separate pathways.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
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

3 authors.

Fa-An ChaoNCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Frederick, MD 21701-4907, United States. Electronic address: fa-an.chao@nih.gov.
Yue ZhangStructural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, United States.
R Andrew ByrdStructural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, United States. Electronic address: byrdra@mail.nih.gov.

Funding

Methods DevelopmentZIABC011132 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI BYRD, ROBERT · 2009 to 2024
$4.6M
CCR NIH HHS HHSN261200800001CIntramural NIH HHS Z99 CA999999Intramural NIH HHS ZIA BC011132NCI NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

NMR relaxation dispersion experiments have been widely applied to probe important conformational exchange of macro-molecules in many biological systems. The current improvements in computational techniques as well as the theoretical breakthroughs make the quantitative data analysis of complex exchange models possible. However, the topology of a given exchange model is also one of the main factors affecting the solution of Bloch-McConnell equation. The lack of a theoretical analysis of the exchange topologies at n-site exchange hinders further progress of such data analysis. Here, using graph theory, we reveal the topological complexity of n-site exchange and present all exchange models when n is less than 6. Furthermore, we introduce an alternative way, using machine learning, to select an exchange model based on a set of relaxation dispersion data without fitting them with every individual exchange model.

Indexed as

Magnetic Resonance ImagingMagnetic Resonance SpectroscopyNuclear Magnetic Resonance, BiomolecularGraph theoryMachine learningNMR relaxation dispersion experimentsN-site exchange

Identifiers

PMID34058714
PMCPMC8205974

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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.