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Record W2072180890 · doi:10.1021/jp304967u

Self-Assembling ADADA Helices Formed by Hydrogen Bonding

2012· article· en· W2072180890 on OpenAlexafffund
Alexis Taylor, Victoria E. J. Berryman, Russell J. Boyd

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2012
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsDalhousie University
FundersAtlantic Canada Opportunities Agency
KeywordsHydrogen bondHelix (gastropod)CrystallographyChemistryRing (chemistry)AcceptorBinding energyAmine gas treatingMonomerPyridineMoleculePolymerOrganic chemistryAtomic physics

Abstract

fetched live from OpenAlex

A computational investigation of the electronic properties of an experimentally prepared ADADA helix indicates that the helix is held together with four strong hydrogen bonds as well as many other weak interactions. Determination of the electronic energy changes, as well as thermodynamic parameters, suggests that helix formation is a favorable process, driven by the formation of the hydrogen bonds. For instance, the unsubstituted helix has an electronic binding energy of -85.8 kJ/mol. Furthermore, the strength of binding can be tuned to some extent by the careful selection of substituents. The hydrogen bonds are strengthened when the pyridine ring (H-bond acceptor) is substituted with an electron-donating group such as an amine, while electron-withdrawing groups on the thiazine ring (H-bond donor) are preferred. The most significant enhancement in binding is achieved when the helix is constructed from monomers that consist of contiguous hydrogen-bond acceptors or donors. This so-called AAAAA-DDDDD helix exhibits a binding energy almost 4-fold greater than that of the unsubstituted ADADA helix at -335.4 kJ/mol, a dramatic improvement over the ADADA helix.

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 score0.006
Threshold uncertainty score0.289

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 science0.0010.000
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 head0.031
GPT teacher head0.318
Teacher spread0.287 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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