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Record W2137426597 · doi:10.1002/jcc.20255

Structure and stability of β‐pleated sheets*

2005· article· en· W2137426597 on OpenAlexaff
András Perczel, Zoltán Gáspári, Imre G. Csizmadia

Bibliographic record

VenueJournal of Computational Chemistry · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAntiparallel (mathematics)CrystallographyBeta sheetProtein secondary structureChemistryProtein structureChemical physicsComputational chemistryPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Beside alpha-helices, beta-sheets are the most common secondary structure elements of proteins. In this article, the question of structure and stability of parallel and antiparallel sheets of various lengths is addressed. All data obtained are compared to a selected set of protein structures. In antiparallel beta-sheets, one of the two possible H-bonded structures (containing 14 atoms in the H-bonded pseudoring) is energetically more favored and also more abundant in proteins than the other one (with 10 atoms involved in the pseudoring). Parallel beta-sheets and their subunits are energetically less stable and indeed found to occur more rarely in proteins. Antiparallel hairpins are disfavored compared to beta-sheets formed by sequentially separated strands. Agreement between theory and experimental data indicates that characterization of structural building blocks at an appropriately accurate level of theory is a useful tool to get insight into fundamentals of protein structure.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.229
Teacher spread0.225 · 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

Citations73
Published2005
Admission routes1
Has abstractyes

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