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Record W1910417335 · doi:10.1039/b003574m

Computer simulation of DNA interacting electrostatically with phosphatidylcholine and trimethylammoniumpropane interfaces

2000· article· en· W1910417335 on OpenAlexfundno aff
David A. Pink, Bonnie Quinn, Jeremy J. Moeller, Rudolf Merkel

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsPhosphatidylcholineChemistryMembraneCrystallographyMoleculeAqueous solutionHydrocarbonDebye lengthLipid bilayerChemical physicsStereochemistryPhospholipidPhysical chemistryOrganic chemistryIon

Abstract

fetched live from OpenAlex

The region of a lipid membrane, with all hydrocarbon chains equal, embedded in an aqueous solution and interacting with a single DNA molecule, has been modelled. The lattice model used assigned an area characteristic of a lipid molecule in a gel phase to each lattice site. In a fluid phase two lipid molecules occupy three sites. We studied a membrane composed of lipids with phosphatidylcholine (PC) and trimethylammoniumpropane (TAP) headgroups. Lipid headgroup states were enumerated as described elsewhere (Pink et al., Biochim. Biophys. Acta, 1988, 1368, 289). Here charged lipid moieties were represented by point charges inside an excluded volume. The aqueous solution was modelled as a linearized Poisson–Boltzmann system characterized by a Debye screening length, κ−1. We employed standard Monte Carlo computer simulation techniques. We came to the following conclusions. (a) In the absence of DNA, PC and TAP headgroup pairs formed dynamic bound states in a gel phase. These did not occur if the PC was represented as an object carrying no charges. Accordingly, although PC carries zero net charge, it is important to represent the charged moieties explicitly. The gel–fluid phase transition in a 1:1 PC–TAP membrane (with equal hydrocarbon chain lengths) might thus involve not only hydrocarbon chain disordering but also the break-up of the dynamical PC–TAP bound pairs. (b) Increasing TAP concentration resulted in changing the orientation of the PC dipole. (c) DNA binding is a complex process and can involve weak binding even to a pure PC interface (which could, however, be disrupted by membrane undulations not modelled here) and tighter binding when TAP is present. The minimum concentration for the latter depends upon κ. DNA binding at low TAP concentrations would be changed if the PC was represented by a chargeless object. (d) A bound DNA changes the headgroup orientation in its proximity and also results in increased headgroup lateral packing in the immediate neighbourhood of the DNA. The latter could result in denser lateral hydrocarbon chain packing in the neighbourhood of the DNA. Both of these phenomena exhibited a dependence upon the TAP concentration. (e) The average lipid–DNA binding energies per DNA PO2− group can be as large as 2kBT or more depending upon the TAP concentration. (f ) DNA can be unbound by changing the ionic concentration. We compare our results with experimental data and other simulations.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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
GenreEmpirical

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

Citations7
Published2000
Admission routes1
Has abstractyes

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