Evidence map›Paper›PMID 28510144›Full record

ReviewBiophysical reviews2016

Enhanced sampling simulations to construct free-energy landscape of protein-partner substrate interaction.

Jinzen Ikebe, Koji Umezawa, Junichi Higo

Abstract readReview
In one paragraph

Review in Biophysical reviews, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction.Journal of chemical theory and computation · 2025
    Article
  5. Article
  6. Article
  7. Computational and structural biotechnology journal · 2022
    Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Article
  17. Review
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.

Jinzen IkebeMolecular Modeling and Simulation Group, Japan Atomic Energy Agency, 8-1-7 Umemidai, Kizugawa, Kyoto, 619-0215, Japan.
Koji UmezawaDepartment of Pure and Applied Physics, Waseda University, Okubo 3-4-1, Shinjuku-Ku, Tokyo, 169-8555, Japan.
Junichi HigoInstitute for Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan. higo@protein.osaka-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Molecular dynamics (MD) simulations using all-atom and explicit solvent models provide valuable information on the detailed behavior of protein-partner substrate binding at the atomic level. As the power of computational resources increase, MD simulations are being used more widely and easily. However, it is still difficult to investigate the thermodynamic properties of protein-partner substrate binding and protein folding with conventional MD simulations. Enhanced sampling methods have been developed to sample conformations that reflect equilibrium conditions in a more efficient manner than conventional MD simulations, thereby allowing the construction of accurate free-energy landscapes. In this review, we discuss these enhanced sampling methods using a series of case-by-case examples. In particular, we review enhanced sampling methods conforming to trivial trajectory parallelization, virtual-system coupled multicanonical MD, and adaptive lambda square dynamics. These methods have been recently developed based on the existing method of multicanonical MD simulation. Their applications are reviewed with an emphasis on describing their practical implementation. In our concluding remarks we explore extensions of the enhanced sampling methods that may allow for even more efficient sampling.

Indexed as

Conformational ensembleEnhanced samplingMolecular dynamics simulationMulticanonicalProtein interaction

Identifiers

PMID28510144
PMCPMC5425738

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.