Evidence map›Paper›PMID 27924273›Full record

ArticleBiophysics and physicobiology2016

Molecular dynamics analysis to evaluate docking pose prediction.

Takako Sakano, Md Iqbal Mahamood, Takefumi Yamashita, Hideaki Fujitani

Abstract read
In one paragraph

Article in Biophysics and physicobiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Article
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  4. ADMET & DMPK · 2026
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  7. Screening of Natural Compounds as Inhibitor of MCurrent pharmaceutical design · 2025
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  10. Article
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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

4 authors.

Takako SakanoLaboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, the University of Tokyo, Tokyo 153-8904, Japan.
Md Iqbal MahamoodLaboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, the University of Tokyo, Tokyo 153-8904, Japan.
Takefumi YamashitaLaboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, the University of Tokyo, Tokyo 153-8904, Japan.
Hideaki FujitaniLaboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, the University of Tokyo, Tokyo 153-8904, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate prediction of a ligand-protein complex structure is important for computer-assisted drug development. Although many docking methods have been developed over the last three decades, the success of binding structure prediction remains greatly limited. The purpose of this study was to demonstrate the usefulness of molecular dynamics (MD) simulation in assessing a docking pose predicted using a docking program. If the predicted pose is not unstable in an aqueous environment, MD simulation equilibrates the system and removes the ligand from the predicted position. Here we investigated two proteins that are important potential therapeutic targets: β2 adrenergic receptor (β2AR) and PR-Set7. While β2AR is rigid and its ligands are very similar to the template ligand (carazolol), PR-Set7 is very flexible and its ligands vary greatly from the template ligand (histone H4 tail peptide). On an empirical basis, we usually expect that the docking prediction is accurate when the protein is rigid and its ligands are similar to the template ligand. The MD analyses in this study clearly suggested such a tendency. Furthermore, we discuss the possibility that the MD simulation can predict the binding pose of a ligand.

Indexed as

drug designG-protein coupled receptorhistone methyltransferaseligand–protein complexmolecular dynamics simulation

Identifiers

PMID27924273
PMCPMC5042163

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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.