Many-body dissipative particle dynamics simulations of lipid bilayers with the MDPD-MARTINI force-field

Abstract Many-body dissipative particle dynamics (MDPD) offers a significant speed-up in the simulation of various systems, including soft matter, in comparison with molecular dynamics (MD) simulations based on Lennard–Jones interactions, which is crucial for describing phenomena characterized by large time and length scales. Moreover, it has recently been shown that the MARTINI force-field coarse-graining approach is applicable in MDPD, thus rendering feasible the simulation of complex molecules as in MD-MARTINI for ever larger systems and longer physical times. Here, simulations of various lipid membranes were performed by using the MDPD-MARTINI coarse-grained (CG) force-field (FF), relevant properties were calculated, and comparison with standard MD-MARTINI CG simulations and experimental data was made. Thus, insights into structural properties of these bilayer systems and further evidence regarding the transferability of the MDPD-MARTINI CG models are provided. In this regard, this is a natural next step in the development of the general-purpose MDPD-MARTINI-FF, which generally provides significant speed-ups in both computational and physical simulated times, in comparison with standard CG MD simulations. Graphical abstract

Authors

Institutions

Publication Details

Journal
The European Physical Journal Plus
Published
2025-06-24
DOI
https://doi.org/10.1140/epjp/s13360-025-06533-4
Citations
3
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
2.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Many-body dissipative particle dynamics simulations of lipid bilayers with the MDPD-MARTINI force-field

Panagiotis E. Theodorakis, Luís H. Carnevale, Natalia Kramarz
3 citations
The European Physical Journal Plus
Protein Structure and Dynamics
2.42
article

Many-body dissipative particle dynamics simulations of lipid bilayers with the MDPD-MARTINI force-field

Panagiotis E. Theodorakis, Luís H. Carnevale, Natalia Kramarz
article en
3 citations

Abstract

Abstract Many-body dissipative particle dynamics (MDPD) offers a significant speed-up in the simulation of various systems, including soft matter, in comparison with molecular dynamics (MD) simulations based on Lennard–Jones interactions, which is crucial for describing phenomena characterized by large time and length scales. Moreover, it has recently been shown that the MARTINI force-field coarse-graining approach is applicable in MDPD, thus rendering feasible the simulation of complex molecules as in MD-MARTINI for ever larger systems and longer physical times. Here, simulations of various lipid membranes were performed by using the MDPD-MARTINI coarse-grained (CG) force-field (FF), relevant properties were calculated, and comparison with standard MD-MARTINI CG simulations and experimental data was made. Thus, insights into structural properties of these bilayer systems and further evidence regarding the transferability of the MDPD-MARTINI CG models are provided. In this regard, this is a natural next step in the development of the general-purpose MDPD-MARTINI-FF, which generally provides significant speed-ups in both computational and physical simulated times, in comparison with standard CG MD simulations. Graphical abstract

The European Physical Journal PlusVol. 140(6)
Polish Academy of Sciences (PL)
Narodowe Centrum Nauki, Narodowym Centrum Nauki, Infrastruktura PL-Grid, Academic Computer Centre Cyfronet, AGH University of Science and Technology
Openalex Percentile: Top 11%
Protein Structure and Dynamics
2.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.