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Record W2978696133 · doi:10.1073/pnas.1911181116

How internal cavities destabilize a protein

2019· article· en· W2978696133 on OpenAlexfundno aff
Mengjun Xue, Takuro Wakamoto, Camilla Kejlberg, Yuichi Yoshimura, Tania A. Nielsen, Michael W. Risør, Kristian W. Sanggaard, Ryo Kitahara, Frans A. A. Mulder

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceUniversity of TorontoCarlsbergfondetMinistry of Education, Culture, Sports, Science and Technology
KeywordsComputer science

Abstract

fetched live from OpenAlex

Significance Proteins exist as ensembles of microstates governed by a free energy landscape, with multiple “excited” states coexisting with the minimum energy structure. These alternate folded and partially disordered states are continuously being accessed through protein dynamics and are key elements required for a comprehensive understanding of protein function and stability. Unfortunately, their low abundance makes these “invisible” states hard to characterize experimentally. A unique view of the hierarchy of unfolding states on the protein energy landscape was obtained here using pressure perturbation. Furthermore, pressure perturbation can directly identify empty protein cavities and determine the energetic penalty of filling these with water.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.259
Teacher spread0.245 · 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 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

Citations57
Published2019
Admission routes1
Has abstractyes

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Same venueProceedings of the National Academy of SciencesSame topicProtein Structure and DynamicsFrench-language works237,207