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Enregistrement W98575952 · doi:10.1021/ar4002729

The Importance of Membrane Defects—Lessons from Simulations

2014· article· en· W98575952 sur OpenAlex

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Notice bibliographique

RevueAccounts of Chemical Research · 2014
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueLipid Membrane Structure and Behavior
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMolecular dynamicsMembraneLipid bilayerChemical physicsTransmembrane proteinBiophysicsChemistryNanotechnologyBiological membraneMaterials scienceComputational chemistryBiochemistryBiology

Résumé

récupéré en direct d'OpenAlex

The defects and pores within lipid membranes are scientifically interesting and have a number of biological applications. Although lipid bilayers are extremely thin hydrophobic barriers, just ∼3 nm thick, they include diverse chemistry and have complex structures. Bilayers are soft and dynamic, and as a result, they can bend and deform in response to different stimuli by means of structural changes in their component lipids. Though defects occur within these structures, their transience and small size have made it difficult to characterize them. However, with recent advances in computer power and computational modeling techniques, researchers can now use simulations as a powerful tool to probe the mechanism and energies of defect and pore formation in a number of situations. In this Account, we present results from our detailed molecular dynamics computer simulations of hydrophilic pores and related defects in lipid bilayers at an atomistic level. Electroporation can be used to increase the permeability of cellular membranes, with potential therapeutic applications. Atomistic simulations of electroporation have illustrated the molecular details of this process, including the importance of water dipole interactions at the water-membrane interface. Characterization of the lipid-protein interactions provides an important tool for understanding transmembrane protein structure and thermodynamic stability. Atomistic simulations give a detailed picture of the free energies of model peptides and side chains in lipid membranes; the energetic cost of defect formation strongly influences the energies of interactions between lipids and polar and charged residues. Many antimicrobial peptides form hydrophilic pores in lipid membranes, killing bacteria or cancer cells. On the basis of simulation data, at least some of these peptides form defects and pores near the center of the bilayer, with a common disordered structure where hydrated headgroups form an approximately toroidal shape. The localization and trafficking of lipids supports general membrane structure and a number of important signaling cascades, such as those involving ceramide, diacylglycerol, and cholesterol. Atomistic simulations have determined the rates and free energies of lipid flip-flop. During the flip-flop of most phosphatidylcholine lipids, a hydrophilic pore forms when the headgroup moves near the center of the bilayer. Simulations have provided novel insight into many features of defects and pores in lipid membranes. Simulation data from very different systems and models show how water penetration and defect formation can determine the free energies of many membrane processes. Bilayers can deform and allow transient defects and pores when exposed to a diverse range of stimuli. Future work will explore many aspects of membrane defects with increased resolution and scope, including the study of more complex lipid mixtures, membrane domains, and large-scale membrane remodeling. Such studies will examine processes including vesicle budding and fusion, non-bilayer lipid phases, and interactions between lipid bilayers and other biomolecules. Simulations provide information that complements experimental studies, allowing microscopic insight into experimental observations and suggesting novel hypotheses and experiments. These studies should enable a deeper understanding of the role of lipid bilayers in cellular biology and support the development of future lipid-based biotechnology.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,232

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,373
Écart entre enseignants0,340 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle