MétaCan
Menu
Back to cohort
Record W2002836933 · doi:10.1110/ps.34101

A model of dynamic side‐chain–side‐chain interactions in the α‐lactalbumin molten globule

2001· article· en· W2002836933 on OpenAlexaff
Ping Bai, Jianxing Song, Li Luo, Zhengyu Peng

Bibliographic record

VenueProtein Science · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsBiotechnology Research Institute
FundersNational Institute of General Medical SciencesNational Institutes of Health
KeywordsMolten globuleSide chainChemistryFolding (DSP implementation)Protein foldingAlanineMutantCrystallographyAlpha-lactalbuminBiophysicsLactalbuminBiochemistryAmino acidOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Proteins in the molten globule state contain high levels of secondary structure, as well as a rudimentary, nativelike tertiary topology. Thus, the structural similarity between the molten globule and native proteins may have a significant bearing in understanding the protein-folding problem. To explore the nature of side-chain--side-chain interactions in the alpha-lactalbumin (alpha-LA) molten globule, we determined the effective concentration for formation of the 28--111 disulfide bond in 14 double-mutant proteins, each containing two hydrophobic core residues replaced by alanine. We compared our results with those of single-alanine substitutions using the framework of double-mutant cycle analysis and found that, in the majority of cases, the effects of two alanine substitutions are additive. Based on these results, we propose a model of side-chain-side-chain interactions in the alpha-LA molten globule, which takes into consideration the dynamic nature of this partially folded species.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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

Citations15
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueProtein ScienceSame topicProtein Structure and DynamicsFrench-language works237,207