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Record W1966655925 · doi:10.1021/jp806183v

Hydration Water Dynamics Near Biological Interfaces

2008· article· en· W1966655925 on OpenAlexaff
Margaret E. Johnson, Cecile Malardier‐Jugroot, Rajesh K. Murarka, Teresa Head‐Gordon

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsRoyal Military College of Canada
FundersNIST Center for Neutron ResearchNational Institute of Standards and TechnologyNational Energy Research Scientific Computing CenterU.S. Department of Energy
KeywordsMolecular dynamicsAmphiphileChemical physicsPolarizabilityChemistryPeptideSolvationArrhenius equationPolarCrystallographyComputational chemistryMoleculePhysical chemistryPhysicsOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

We performed classical molecular dynamics simulations using both fixed-charge and polarizable water and protein force fields to contrast the hydration dynamics near hydrophilic and amphiphilic peptides as a function of temperature. The high peptide concentrations we use serve as a model for the surface of folded proteins where hydration layers around each residue overlap significantly. Through simulation we determine that there are notable differences in the water dynamics analyzed from the outer and inner hydration layer regions of the amphiphilic peptide solution that explains the experimentally observed presence of two translational relaxations, while the hydrophilic peptide solution shows only a single non-Arrhenius translational process with no distinction between hydration layers. Given that water dynamics for the amphiphilic peptide system reproduces all known rotational and translational hydration dynamical anomalies exhibited by hydration water near protein surfaces, our analysis provides strong evidence that dynamical signatures near biological interfaces arises because of frustration in the hydration dynamics induced by chemical heterogeneity, as opposed to just topological roughness, of the protein surface.

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 score0.004
Threshold uncertainty score0.192

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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

Citations63
Published2008
Admission routes1
Has abstractyes

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