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Record W98575952 · doi:10.1021/ar4002729

The Importance of Membrane Defects—Lessons from Simulations

2014· article· en· W98575952 on OpenAlexaff
William F. Bennett, D. Peter Tieleman

Bibliographic record

VenueAccounts of Chemical Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMolecular dynamicsMembraneLipid bilayerChemical physicsTransmembrane proteinBiophysicsChemistryNanotechnologyBiological membraneMaterials scienceComputational chemistryBiochemistryBiology

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.373
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations91
Published2014
Admission routes1
Has abstractyes

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