Evidence map›Paper›PMID 26505632›Full record

ArticlePLoS computational biology2015

ENCORE: Software for Quantitative Ensemble Comparison.

Matteo Tiberti, Elena Papaleo, Tone Bengtsen, Wouter Boomsma, Kresten Lindorff-Larsen

Abstract readComparative Study
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

  1. Article
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  10. Protein ensemble modeling and analysis with MMMx.Protein science : a publication of the Protein Society · 2024
    Article
  11. Article
  12. Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0.Acta crystallographica. Section D, Structural biology · 2022
    Review
  13. Article
  14. MgeLife · 2022
    Article
  15. Article
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  17. Article
  18. Article
  19. Article
  20. Finding Druggable Sites in Proteins Using TACTICS.Journal of chemical information and modeling · 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

5 authors.

Matteo TibertiDepartment of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy; Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Elena PapaleoStructural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Tone BengtsenStructural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Wouter BoomsmaStructural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Kresten Lindorff-LarsenStructural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large trajectory files.

Indexed as

AlgorithmsMolecular Dynamics SimulationSoftwarePattern Recognition, AutomatedProgramming LanguagesProtein ConformationProteinsSoftware ValidationProteins

Identifiers

PMID26505632
PMCPMC4624683

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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.