Evidence map›Paper›PMID 27578633›Full record

ArticleNature communications2016

Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations.

Laura Orellana, Ozge Yoluk, Oliver Carrillo, Modesto Orozco, Erik Lindahl

Abstract read
In one paragraph

Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing 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

46 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. A two-step mechanism for sugar translocation.Nature structural & molecular biology · 2026
    Article
  5. Improving Conformational Ensembles of Folded Proteins in Go̅Martini.Journal of chemical theory and computation · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Applications of Molecular Dynamics Simulations in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024
    Review
  15. Statistical learning of protein elastic network from positional covariance matrix.Computational and structural biotechnology journal · 2023
    Article
  16. Article
  17. Review
  18. Article
  19. Review
  20. 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.

Laura OrellanaDepartment of Theoretical Physics, KTH Royal Institute of Technology, Box 1031, 171 21 Solna, Sweden.ORCID 0000-0003-1927-555X
Ozge YolukDepartment of Theoretical Physics, KTH Royal Institute of Technology, Box 1031, 171 21 Solna, Sweden.
Oliver CarrilloScuola Normale Superiore de Pisa, Dipartimento di Fisica, Piazza dei Cavalieri, 7, 56126 Pisa, Italy.
Modesto OrozcoJoint BSC-IRB Research Program in Computational Biology, Institute for Research in Biomedicine (IRB) Barcelona, C/Baldiri Reixac 10, 08028 Barcelona, Spain.
Erik LindahlDepartment of Theoretical Physics, KTH Royal Institute of Technology, Box 1031, 171 21 Solna, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides a powerful framework to extract and expand dynamic pathway information from the Protein Data Bank, as well as to validate sampling methods in general.

Indexed as

Molecular Dynamics SimulationProtein ConformationAlgorithmsDatabases, ProteinFeasibility StudiesPrincipal Component Analysis

Identifiers

PMID27578633
PMCPMC5013691

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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.