Evidence map›Paper›PMID 26940626›Full record

ReviewBiochimica et biophysica acta2016

Atomic-level description of protein-lipid interactions using an accelerated membrane model.

Javier L Baylon, Josh V Vermaas, Melanie P Muller, Mark J Arcario, Taras V Pogorelov, Emad Tajkhorshid

Open access · greenAbstract readReview
In one paragraph

Review in Biochimica et biophysica acta, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
3.8field-weighted citation impact, top 7% of its field
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

32 citing papers in PubMed, 55 citations in OpenAlex.

  1. Article
  2. CaThe journal of physical chemistry. B · 2025
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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

6 authors at 3 institutions in 2 countries.

Javier L BaylonCenter for Biophysics and Quantitative Biology; Beckman Institute for Advanced Science and Technology. Electronic address: baylonc2@illinois.edu.
Josh V VermaasCenter for Biophysics and Quantitative Biology; Beckman Institute for Advanced Science and Technology. Electronic address: vermaas2@illinois.edu.
Melanie P MullerCenter for Biophysics and Quantitative Biology; Beckman Institute for Advanced Science and Technology; College of Medicine. Electronic address: mmulle23@illinois.edu.
Mark J ArcarioCenter for Biophysics and Quantitative Biology; Beckman Institute for Advanced Science and Technology; College of Medicine. Electronic address: marcar2@illinois.edu.
Taras V PogorelovBeckman Institute for Advanced Science and Technology; School of Chemical Sciences; Department of Chemistry; National Center for Supercomputing Applications. Electronic address: pogorelo@illinois.edu.
Emad TajkhorshidCenter for Biophysics and Quantitative Biology; Beckman Institute for Advanced Science and Technology; College of Medicine; Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL 61801. Electronic address: emad@life.illinois.edu.
Quantitative BioSciences · USNational Center for Supercomputing Applications · BGUniversity of Illinois Urbana-Champaign · US

Funding

Synthetic Antigen Binder CoreU54GM087519 · NIGMS · UNIVERSITY OF CHICAGO · PI ROUX, BENOIT · 2010 to 2017
$31.9M
WHOLE CELL SIMULATIONP41GM104601 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI TAJKHORSHID, EMAD · 2012 to 2021
$19.0M
Molecular Mechanisms of Active Transport Across Cellular MembranesR01GM086749 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI TAJKHORSHID, EMAD · 2009 to 2013
$1.5M
Understanding the role of phospholipids in integrin signalingR01GM101048 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI SLIGAR, STEPHEN G. · 2012 to 2015
$1.1M
NIGMS NIH HHS P41 GM104601NIGMS NIH HHS P41-GM104601NIGMS NIH HHS R01 GM086749NIGMS NIH HHS R01-GM086749NIGMS NIH HHS R01 GM101048NIGMS NIH HHS R01-GM101048NIGMS NIH HHS U54 GM087519NIGMS NIH HHS U54-GM087519
6 · The paper itself

Abstract

Peripheral membrane proteins are structurally diverse proteins that are involved in fundamental cellular processes. Their activity of these proteins is frequently modulated through their interaction with cellular membranes, and as a result techniques to study the interfacial interaction between peripheral proteins and the membrane are in high demand. Due to the fluid nature of the membrane and the reversibility of protein-membrane interactions, the experimental study of these systems remains a challenging task. Molecular dynamics simulations offer a suitable approach to study protein-lipid interactions; however, the slow dynamics of the lipids often prevents sufficient sampling of specific membrane-protein interactions in atomistic simulations. To increase lipid dynamics while preserving the atomistic detail of protein-lipid interactions, in the highly mobile membrane-mimetic (HMMM) model the membrane core is replaced by an organic solvent, while short-tailed lipids provide a nearly complete representation of natural lipids at the organic solvent/water interface. Here, we present a brief introduction and a summary of recent applications of the HMMM to study different membrane proteins, complementing the experimental characterization of the presented systems, and we offer a perspective of future applications of the HMMM to study other classes of membrane proteins. This article is part of a Special Issue entitled: Membrane proteins edited by J.C. Gumbart and Sergei Noskov.

Indexed as

Models, ChemicalModels, MolecularBinding SitesComputer SimulationLipid BilayersMembrane FluidityMembrane ProteinsProtein BindingProtein ConformationProtein Interaction MappingLipid BilayersMembrane ProteinsLipidsMembraneMolecular dynamicsPeripheral proteins

Identifiers

PMID26940626
PMCPMC4877275
OpenAlexW2291814221

What OpenQuestion holds

Textmetadata
LicenceTDM
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.