Literature DB >> 24024591

Why do arginine and lysine organize lipids differently? Insights from coarse-grained and atomistic simulations.

Zhe Wu1, Qiang Cui, Arun Yethiraj.   

Abstract

An important puzzle in membrane biophysics is the difference in the behaviors of lysine (Lys) and arginine (Arg) based peptides at the membrane. For example, the translocation of poly-Arg is orders of magnitude faster than that of poly-Lys. Recent experimental work suggests that much of the difference can be inferred from the phase behavior of peptide/lipid mixtures. At similar concentrations, mixtures of phosphatidylethanolamine (PE) and phosphatidylserine (PS) lipids display different phases in the presence of these polypeptides, with a bicontinuous phase observed with poly-Arg peptides and an inverted hexagonal phase observed with poly-Lys peptides. Here we show that simulations with the coarse-grained (CG) BMW-MARTINI model reproduce the experimental results. An analysis using atomistic and CG models reveals that electrostatic and glycerol-peptide interactions play a crucial role in determining the phase behavior of peptide-lipid mixtures, with the difference between Arg and Lys arising from the stronger interactions of the former with lipid glycerols. In other words, the multivalent nature of the guanidinium group allows Arg to simultaneously interact with both phosphate and glycerol groups, while Lys engages solely with phosphate; this feature of amino acid/lipid interactions has not been emphasized in previous studies. The Arg peptides colocalize with PS in regions of high negative Gaussian curvature and stabilize the bicontinuous phase. Decreasing the strength of either the electrostatic interactions or the peptide-glycerol interactions causes the inverted hexagonal phase to become more stable. The results highlight the utility of CG models for the investigation of phase behavior but also emphasize the subtlety of the phenomena, with small changes in specific interactions leading to qualitatively different phases.

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Year:  2013        PMID: 24024591     DOI: 10.1021/jp4068729

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  19 in total

1.  Unifying structural signature of eukaryotic α-helical host defense peptides.

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Journal:  Proc Natl Acad Sci U S A       Date:  2019-03-15       Impact factor: 11.205

2.  HIV-1 Tat membrane interactions probed using X-ray and neutron scattering, CD spectroscopy and MD simulations.

Authors:  Kiyotaka Akabori; Kun Huang; Bradley W Treece; Michael S Jablin; Brian Maranville; Arthur Woll; John F Nagle; Angel E Garcia; Stephanie Tristram-Nagle
Journal:  Biochim Biophys Acta       Date:  2014-08-19

Review 3.  What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?

Authors:  Ernest Y Lee; Michelle W Lee; Benjamin M Fulan; Andrew L Ferguson; Gerard C L Wong
Journal:  Interface Focus       Date:  2017-10-20       Impact factor: 3.906

4.  Relationships between MA-RNA Binding in Cells and Suppression of HIV-1 Gag Mislocalization to Intracellular Membranes.

Authors:  Dishari Thornhill; Balaji Olety; Akira Ono
Journal:  J Virol       Date:  2019-11-13       Impact factor: 5.103

5.  Differential Membrane Binding Mechanics of Synaptotagmin Isoforms Observed in Atomic Detail.

Authors:  Josh V Vermaas; Emad Tajkhorshid
Journal:  Biochemistry       Date:  2016-12-20       Impact factor: 3.162

6.  Investigating Hydrophilic Pores in Model Lipid Bilayers Using Molecular Simulations: Correlating Bilayer Properties with Pore-Formation Thermodynamics.

Authors:  Yuan Hu; Sudipta Kumar Sinha; Sandeep Patel
Journal:  Langmuir       Date:  2015-02-20       Impact factor: 3.882

Review 7.  Machine learning-enabled discovery and design of membrane-active peptides.

Authors:  Ernest Y Lee; Gerard C L Wong; Andrew L Ferguson
Journal:  Bioorg Med Chem       Date:  2017-07-08       Impact factor: 3.641

8.  Unique functional properties of conserved arginine residues in the lentivirus lytic peptide domains of the C-terminal tail of HIV-1 gp41.

Authors:  Anne-Sophie Kuhlmann; Jonathan D Steckbeck; Timothy J Sturgeon; Jodi K Craigo; Ronald C Montelaro
Journal:  J Biol Chem       Date:  2014-02-04       Impact factor: 5.157

9.  Self-association of a highly charged arginine-rich cell-penetrating peptide.

Authors:  Giulio Tesei; Mario Vazdar; Malene Ringkjøbing Jensen; Carolina Cragnell; Phil E Mason; Jan Heyda; Marie Skepö; Pavel Jungwirth; Mikael Lund
Journal:  Proc Natl Acad Sci U S A       Date:  2017-10-11       Impact factor: 11.205

Review 10.  Computational Modeling of Realistic Cell Membranes.

Authors:  Siewert J Marrink; Valentina Corradi; Paulo C T Souza; Helgi I Ingólfsson; D Peter Tieleman; Mark S P Sansom
Journal:  Chem Rev       Date:  2019-01-09       Impact factor: 72.087

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