The Effect of Temperature, Cations, and Number of Acyl Chains on the Lamellar to Non-Lamellar Transition in Lipid-A Membranes: A Microscopic View

Pontes, Frederico J. S.; Rusu, Victor H.; Soares, Thereza A.; Lins, Roberto D.

doi:10.1021/ct300084v

Cited by 54 publications

(93 citation statements)

References 51 publications

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“…After the two leaflets are built, the system is assembled and subjected to a short energy minimization to remove any steric clashes. If any of the leaflets contains LPSs, Ca 2+ ions are packed around the phosphates of Lipid A and the oligosaccharide core to neutralize their charge; this is performed because divalent cations have been found to contribute to the stabilization of the OM, by interacting with the LPS phosphates . In the final step, the user chooses whether the system will be solvated and ionized.…”

Section: Methodsmentioning

confidence: 99%

“…These include variations of Lipid A from four different species ( E. coli , Pseudomonas aeruginosa , Helicobacter pylori and Neisseria meningitidis ) (Supporting Information Fig. S1), as well as three experimentally studied LPSs, containing Lipid A and the oligosaccharide core (Re and Ra LPS for E. coli and PAO1 LPS for P. aeruginosa ) . In addition, a number of standard lipids are also offered, representing the major categories most commonly found in bacteria .…”

Section: Methodsmentioning

confidence: 99%

“…The number of lipid tails and phosphates can be different among LPSs (Fig. S1) . The oligosaccharide core is covalently linked to Lipid A and is composed of a few copies of rare sugars such as 3‐deoxy‐D‐manno‐2‐octulosonic acid (kdo) or L‐glycero‐D‐manno heptose (Hep), as well as standard hexoses such as D‐glucose or D‐galactose.…”

Section: Introductionmentioning

confidence: 99%

“…Because both Lipid A and the core are phosphorylated, LPSs have a high negative charge and require divalent cations such as Ca 2+ or Mg 2+ to form stable membrane structures. This ionic interaction is a major determinant in maintaining the OM's integrity …”

Section: Introductionmentioning

confidence: 99%

“…Some glycolipid and glycan modeling tools can also be used to model LPS molecules, such as doGlycans for the AMBER/GLYCAM and OPLS force fields; however, these methods are not able to construct LPS‐containing bilayers, either homogeneous or mixed. As such, apart from CHARMM‐GUI, there are very limited options to automatically create LPS‐containing membranes with force fields or molecules outside the aforementioned systems (e.g., GROMOS, or MARTINI with LPSs from species other than E. coli ), even though valid Lipid A and LPS parameters exist . Instead, the literature shows that LPS‐containing simulation systems in such cases are, for the most part, built by hand, a process that can be time consuming and difficult, particularly for inexperienced users.…”

Section: Introductionmentioning

confidence: 99%

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The gram‐negative outer membrane modeler: Automated building of lipopolysaccharide‐rich bacterial outer membranes in four force fields

2019

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Outer membranes are a crucial component of Gram‐negative bacteria, containing standard lipids in their inner leaflet, lipopolysaccharides (LPSs) in their outer leaflet, and transmembrane β‐barrels known as outer membrane proteins (OMPs). OMPs regulate functions such as substrate transport and cell movement, while LPSs act as a protective barrier for bacteria and can cause toxic reactions in humans. However, the experimental study of outer membranes is challenging. Molecular dynamics simulations are often used for the computational study of membrane systems, but the preparation of complex, LPS‐rich outer membranes is not straightforward. The Gram‐Negative Outer Membrane Modeler (GNOMM) is an automated pipeline for preparing simulation systems of OMPs embedded in LPS‐containing membranes in four different force fields. Given the physiological and clinical importance of outer membranes and their components, GNOMM can be a useful tool in the study of their structure, function, and implications in diseases. GNOMM is available at http://bioinformatics.biol.uoa.gr/GNOMM. © 2019 Wiley Periodicals, Inc.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The gram‐negative outer membrane modeler: Automated building of lipopolysaccharide‐rich bacterial outer membranes in four force fields

2019

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Gram‐Negative Bacteria Targeting Mediated by Carbohydrate–Carbohydrate Interactions Induced by Surface‐Modified Nanoparticles

Capeletti

Oliveira

Loiola

et al. 2019

Adv Funct Materials

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Antibiotic resistant pathogens are a modern threat to the human health. As a worldwide spreading problem, there is an urgency for new strategies to minimize antibiotic resistance, particularly the super‐resistant strains. Here, the efficient design of carbohydrate‐coated silica nanoparticles is reported which specifically target Gram‐negative bacteria cells. The system is functionalized with gluconamide moieties and demonstrates increased binding ability to the bacterial membrane, enabling controlled drug delivery onto the pathogen wall. In addition, the high stability of the nanoparticles in biological media and the lack of non‐specific protein adhesion are engendered by such functionalization, which also demonstrates low cytotoxicity and hemolytic activity prevention. Local interaction between nanoparticles and the bacterium membrane is experimentally accessed at the biomolecular level unveiling a short‐range chemical connection. Atomistic molecular dynamics simulations depict the rapid penetration of gluconamide in the lipopolysaccharide region of the bacterial outer membrane, corroborating the experimental findings. Thus, this novel outer membrane‐targeting platform provides a new strategy to reduce drug intake and, hence, minimize bacterial resistance.

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Molecular Dynamics Simulation of Small Molecules Interacting with Biological Membranes

et al. 2020

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Cell membranes protect and compartmentalise cells and their organelles. The semi-permeable nature of these membranes controls the exchange of solutes across their structure. Characterising the interaction of small molecules with biological membranes is critical to understanding of physiological processes, drug action and permeation, and many biotechnological applications. This review provides an overview of how molecular simulations are used to study the interaction of small molecules with biological membranes, with a particular focus on the interactions of water, organic compounds, drugs and short peptides with models of plasma cell membrane and stratum corneum lipid bilayers. This review will not delve on other type of membranes which might have different composition and arrangement, such as thylakoid or mitochondrial membranes. The application of unbiased molecular dynamics simulations and enhanced sampling methods such as umbrella sampling, metadynamics and replica exchange are described using key examples. This review demonstrates how state-of-the-art molecular simulations have been used successfully to describe the mechanism of binding and permeation of small molecules with biological membranes, as well as associated changes to the structure and dynamics of these membranes. The review concludes with an outlook on future directions in this field.

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The Effect of Temperature, Cations, and Number of Acyl Chains on the Lamellar to Non-Lamellar Transition in Lipid-A Membranes: A Microscopic View

Cited by 54 publications

References 51 publications

The gram‐negative outer membrane modeler: Automated building of lipopolysaccharide‐rich bacterial outer membranes in four force fields

The gram‐negative outer membrane modeler: Automated building of lipopolysaccharide‐rich bacterial outer membranes in four force fields

Gram‐Negative Bacteria Targeting Mediated by Carbohydrate–Carbohydrate Interactions Induced by Surface‐Modified Nanoparticles

Molecular Dynamics Simulation of Small Molecules Interacting with Biological Membranes

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