Evidence map›Paper›PMID 33596657›Full record

ArticleThe journal of physical chemistry letters2021

Inference of Joint Conformational Distributions from Separately Acquired Experimental Measurements.

Jennifer M Hays, Emily Boland, Peter M Kasson

Abstract read
In one paragraph

Article in The journal of physical chemistry letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Jennifer M HaysDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22904-4259, United States.
Emily BolandDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22904-4259, United States.
Peter M KassonDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22904-4259, United States.ORCID http://orcid.org/0000-0002-3111-8103

Funding

Hardening and Development of the GROMACS Molecular Simulation EngineR01GM115790 · NIGMS · UNIVERSITY OF VIRGINIA · PI KASSON, PETER M, SHIRTS, MICHAEL R · 2016 to 2020
$1.1M
NIGMS NIH HHS R01 GM115790
6 · The paper itself

Abstract

Flexible proteins serve vital roles in a multitude of biological processes. However, determining their full conformational ensembles is extremely difficult because this requires detailed knowledge about the heterogeneity of the protein's degrees of freedom. Label-based experiments such as double electron-electron resonance (DEER) are very useful in studying flexible proteins, as they provide distributional data on heterogeneity. These experiments are typically performed separately, so information about correlation between distributions is lost. We have developed a method to recover correlation information using nonequilibrium work estimates in molecular dynamics refinement. We tested this method on a simple model of an alternating-access transporter for which the true joint distributions are known, and it successfully recovered the true joint distribution. We also applied our method to the protein syntaxin-1a, where it discarded physically implausible conformations. Our method thus provides a way to recover correlation structure in separate experimental measurements of conformational ensembles and refines the resulting structural ensemble.

Identifiers

PMID33596657
PMCPMC8310705

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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.