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Record W2148430681 · doi:10.1021/bm1008074

Protein Specificity of Charged Sequences in Polyanions and Heparins

2010· article· en· W2148430681 on OpenAlexafffund
A. Basak Kayitmazer, Bonnie Quinn, Kozue Kimura, Gillian L. Ryan, Angela J. Tate, David A. Pink, Paul L. Dubin

Bibliographic record

VenueBiomacromolecules · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsSt. Francis Xavier University
FundersDivision of ChemistryAdvanced Foods and Materials NetworkNational Science Foundation
KeywordsChemistryElectrostaticsSulfationStatic electricityHeparinoidCharge densityBiophysicsBinding selectivityIntrinsically disordered proteinsChemical physicsComputational chemistryBiochemistryHeparinPhysical chemistry

Abstract

fetched live from OpenAlex

Long-range electrostatic interactions are generally assigned a subordinate role in the high-affinity binding of proteins by glycosaminoglycans, the most highly charged biopolyelectrolytes. The discovery of high and low sulfation domains in heparan sulfates, however, suggests selectivity via complementarity of their linear sulfation patterns with protein charge patterns. We examined how charge sequences in anionic/nonionic copolymers affect their binding to a protein with prominent charge anisotropy. Experiments and united-atom Monte Carlo simulations, together with Delphi electrostatic modeling for the protein, confirm strongest binding when polyanion sequences allow for optimization of repulsive and attractive electrostatics. Simulations also importantly identified retention of considerable polyion conformational freedom, even for strong binding. The selective affinity for heparins of high and low charge density found for this protein is consistent with nonspecific binding to distinctly different protein charge domains. These findings suggest a more nuanced view of specificity than previously proposed for heparinoid-binding proteins.

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 score0.011
Threshold uncertainty score0.460

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 head0.011
GPT teacher head0.266
Teacher spread0.256 · 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

Citations52
Published2010
Admission routes2
Has abstractyes

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