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Record W2053178770 · doi:10.1002/bip.10538

Hydrophobic tendencies of polar groups as a major force in molecular recognition

2003· article· en· W2053178770 on OpenAlexaff
Tigran V. Chalikian

Bibliographic record

VenueBiopolymers · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistryHydrogen bondPolarSteric effectsHydrophobic effectNucleic acidFolding (DSP implementation)SolventMacromoleculeCrystallographyMolecular recognitionChemical physicsMoleculeStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Proteins and nucleic acids are able to adopt their native conformation and perform their biological role only in the presence of water with which they actively interact in a mutually modifying way. Traditionally, hydrophobic effect has been considered to be the major factor stabilizing biopolymeric structures. However, solvent reorganization around polar groups is an event thermodynamically more unfavorable than solvent reorganization around nonpolar groups. Consequently, burial of polar groups with formation of complementary solute-solute hydrogen bonds out of contact with water is an energetically favorable process that also provides a major force driving macromolecular association and folding. In contrast to nonpolar groups, polar groups may form their complementary intra- or intersolute hydrogen bonds out of contact with water only provided that an appropriate solute structure has been formed with properly positioned hydrogen bond donors and acceptors. Formation of such structures is disfavored entropically and may not be possible due to steric reasons. However, the interior of a folded protein, alpha-helices and beta-sheets, double helical nucleic acid structures, and protein-ligand interfaces all provide rigid matrices where polar groups may form their complementary hydrogen bonds. For these structures, the inward drive of polar groups represents a considerable stabilizing factor.

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 score0.013
Threshold uncertainty score0.581

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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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

Citations25
Published2003
Admission routes1
Has abstractyes

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