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Record W2318005401 · doi:10.1021/jp308132y

Potential of Mean Force of Polyethylenimine-Mediated DNA Attraction

2012· article· en· W2318005401 on OpenAlexaff
Sampada Bagai, Chongbo Sun, Tian Tang

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPolyethylenimineProtonationMoleculeChemistryDNAPotential of mean forceCationic polymerizationCharge (physics)Molecular dynamicsChemical physicsAnalytical Chemistry (journal)CrystallographyComputational chemistryIonPhysicsChromatographyPolymer chemistryOrganic chemistryQuantum mechanicsBiochemistry

Abstract

fetched live from OpenAlex

The aggregation of DNA molecules induced by cationic polymers is of importance to applications in gene delivery. In this work, we performed a series of umbrella sampling molecular dynamics simulations to calculate the potential of mean force (PMF) between two DNA molecules in the presence of polyethylenimine (PEI) molecules using the weighted histogram analysis method. The distance between the centers of mass of the two DNAs was chosen as the reaction coordinate, and the location and depth of the global minimum in the PMF curve were used to gauge the compactness and stability of the formed aggregate. The effects of PEI to DNA charge ratio (N/P charge ratio) and protonation state of the PEI were investigated. The DNA aggregation was found to be more favorable at higher PEI/DNA charge ratios and higher PEI protonation ratios. As compared with small multivalent ions, PEIs give rise to stronger DNA attraction even with an N/P charge ratio at which the DNAs are overneutralized. The effect of changing the protonation ratio on the compactness of the aggregate is more prominent than changing the N/P charge ratio, while the depth of the PMF well is more strongly influenced by the N/P charge ratio.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations23
Published2012
Admission routes1
Has abstractyes

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