Evidence map›Paper›PMID 41205699›Full record

ArticleBiophysical reports2025

Optimizing a coarse-grained model for large-scale membrane protein simulation.

Chen Yun Wen, Yun Lyna Luo, Jesper J Madsen

Abstract read
In one paragraph

Article in Biophysical reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Chen Yun WenDepartment of Biotechnology and Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, California.
Yun Lyna LuoDepartment of Biotechnology and Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, California. Electronic address: luoy@westernu.edu.
Jesper J MadsenDepartment of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida; Center for Global Health and Infectious Diseases Research, Global and Planetary Health, College of Public Health, University of South Florida, Tampa, Florida. Electronic address: jespermadsen@usf.edu.

Funding

PHARMACOLOGICAL MODULATION OF PIEZO1 CHANNELSR01GM130834 · NIGMS · WESTERN UNIVERSITY OF HEALTH SCIENCES · PI Yun Lyna LUO, Jerome Lacroix · 2019 to 2026
$2.8M
Cooperative Modulation of Piezo ChannelsR01HL179692 · NHLBI · WESTERN UNIVERSITY OF HEALTH SCIENCES · PI Yun Lyna LUO, Jerome Lacroix · 2025 to 2026
$1.3M
NHLBI NIH HHS R01 HL179692NIGMS NIH HHS R01 GM130834
6 · The paper itself

Abstract

Coarse-grained (CG) models are widely used to study membrane proteins at physiologically relevant scales. However, simulating long-range bilayer deformations induced by membrane-embedded proteins at submicrometer scales remains challenging. Here, we assess a generic solvent-free CG lipid model, previously applied to membrane proteins, for large-scale molecular dynamics simulations. We find that beyond a critical membrane size, the model becomes unstable due to membrane poration and unphysical undulations. To overcome this limitation, we systematically optimize this lipid model, significantly extending its stability for larger membrane systems. Using this improved model, we simulate membrane deformation induced by the mechanosensitive ion channel PIEZO in bilayers with varying mechanical properties. This optimized CG model with tunable mechanical properties provides a timely tool for investigating bilayer-mediated membrane protein interactions and bridging the gap between continuum elasticity theory and atomistic simulations.

Indexed as

Membrane ProteinsMolecular Dynamics SimulationIon ChannelsLipid BilayersIon ChannelsLipid BilayersMembrane Proteins

Identifiers

PMID41205699
PMCPMC12670456

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.