Evidence map›Paper›PMID 20515115›Full record

ArticleThe Journal of chemical physics2010

An implicit solvent coarse-grained lipid model with correct stress profile.

Alex J Sodt, Teresa Head-Gordon

Abstract read
In one paragraph

Article in The Journal of chemical physics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. The power of coarse graining in biomolecular simulations.Wiley interdisciplinary reviews. Computational molecular science · 2014
    Article
  10. Atomistic Monte Carlo simulation of lipid membranes.International journal of molecular sciences · 2014
    Review
  11. A Hybrid Approach for Highly Coarse-grained Lipid Bilayer Models.Journal of chemical theory and computation · 2013
    Article
  12. Article
  13. Article
  14. 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

2 authors.

Alex J SodtDepartment of Bioengineering, University of California, Berkeley, Berkeley, California 94720, USA.
Teresa Head-Gordon

Funding

Experimental/Computational Study of Protein AggregationR01GM070919 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI HEAD-GORDON, TERESA L. · 2006 to 2009
$1.1M
NIGMS NIH HHS R01 GM070919NIGMS NIH HHS R01GM070919
6 · The paper itself

Abstract

We develop a coarse-grained parametrization strategy for lipid membranes that we illustrate for a dipalmitoylphosphatidylcholine bilayer. Our coarse-graining approach eliminates the high cost of explicit solvent but maintains more lipid interaction sites. We use a broad attractive tail-tail potential and extract realistic bonded potentials of mean force from all-atom simulations, resulting in a model with a sharp gel to fluid transition, a correct bending modulus, and overall very reasonable dynamics when compared with experiment. We also determine a quantitative stress profile and correct breakdown of contributions from lipid components when compared with detailed all-atom simulation benchmarks, which has been difficult to achieve for implicit membrane models. Such a coarse-grained lipid model will be necessary for efficiently simulating complex constructs of the membrane, such as protein assembly and lipid raft formation, within these nonaqueous chemical environments.

Indexed as

Computer SimulationThermodynamics1,2-DipalmitoylphosphatidylcholineEdible GrainLipid BilayersLipidsMembranesModels, MolecularSolvents1,2-Dipalmitoylphosphatidylcholinecolfosceril palmitateLipid BilayersLipidsSolvents

Identifiers

PMID20515115
PMCPMC2887916

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.