Recalibration of MARTINI‑3 Parameters for Improved\nInteractions between Peripheral Proteins and Lipid Bilayers

The\nMARTINI force field is one of the most used coarse-grained\nmodels for biomolecular simulations. Many limitations of the model\nincluding the protein–protein overaggregation have been improved\nin its latest version, MARTINI-3. In this study, we investigate the\nefficacy of the MARTINI-3 parameters for capturing the interactions\nof peripheral proteins with model plasma membranes. Particularly,\nwe consider two classes of proteins, namely, annexin and epsin, which\nare known to generate negative and positive membrane curvatures, respectively.\nWe find that current MARTINI-3 parameters are not able to correctly\ndescribe the protein–membrane interface and the protein-induced\nmembrane curvatures for any of these proteins. The problem arises\ndue to the lack of proper hydrophobic interactions between the protein\nresidues and lipid tails. Making systematic adjustments, we show that\na combination of reduction in the protein–water interactions\nand enhancement of protein–lipid hydrophobic interactions is\nessential for accurate prediction of the interfacial structure including\nthe protein-induced membrane curvature. Next, we apply our model to\na couple of other peripheral proteins, namely, Snf7, a core component\nof the ESCRT-III complex, and the PH domain of evectin-2. We find\nthat our model captures the protein–membrane interfacial structure\nmuch more accurately than the MARTINI-3 model for all of the peripheral\nproteins considered in this study. However, the strategy described\nin this study may not be suitable for oligomeric transmembrane proteins\nwhere protein–protein hydrophobic interactions should be increased\ninstead of protein–lipid hydrophobic interactions.

Publication Details

Journal
Figshare
Published
2024-11-04
DOI
https://doi.org/10.1021/acs.jctc.4c00645.s001
Primary Topic
Lipid Membrane Structure and Behavior
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recalibration of MARTINI‑3 Parameters for Improved\nInteractions between Peripheral Proteins and Lipid Bilayers

Figshare
Lipid Membrane Structure and Behavior
article

Recalibration of MARTINI‑3 Parameters for Improved\nInteractions between Peripheral Proteins and Lipid Bilayers

article en

Abstract

The\nMARTINI force field is one of the most used coarse-grained\nmodels for biomolecular simulations. Many limitations of the model\nincluding the protein–protein overaggregation have been improved\nin its latest version, MARTINI-3. In this study, we investigate the\nefficacy of the MARTINI-3 parameters for capturing the interactions\nof peripheral proteins with model plasma membranes. Particularly,\nwe consider two classes of proteins, namely, annexin and epsin, which\nare known to generate negative and positive membrane curvatures, respectively.\nWe find that current MARTINI-3 parameters are not able to correctly\ndescribe the protein–membrane interface and the protein-induced\nmembrane curvatures for any of these proteins. The problem arises\ndue to the lack of proper hydrophobic interactions between the protein\nresidues and lipid tails. Making systematic adjustments, we show that\na combination of reduction in the protein–water interactions\nand enhancement of protein–lipid hydrophobic interactions is\nessential for accurate prediction of the interfacial structure including\nthe protein-induced membrane curvature. Next, we apply our model to\na couple of other peripheral proteins, namely, Snf7, a core component\nof the ESCRT-III complex, and the PH domain of evectin-2. We find\nthat our model captures the protein–membrane interfacial structure\nmuch more accurately than the MARTINI-3 model for all of the peripheral\nproteins considered in this study. However, the strategy described\nin this study may not be suitable for oligomeric transmembrane proteins\nwhere protein–protein hydrophobic interactions should be increased\ninstead of protein–lipid hydrophobic interactions.

Figshare
Openalex Percentile: Top 76%
Lipid Membrane Structure and Behavior
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.