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Record W2535374949 · doi:10.1073/pnas.1611418113

Probing the free energy landscapes of ALS disease mutants of SOD1 by NMR spectroscopy

2016· article· en· W2535374949 on OpenAlexafffund
Ashok Sekhar, Jessica Rumfeldt, Helen R. Broom, Colleen Doyle, Ryan E. Sobering, Elizabeth M. Meiering, Lewis E. Kay

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsHospital for Sick ChildrenUniversity of WaterlooUniversity of Toronto
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsSOD1Conformational isomerismMutantChemistryProtein foldingAmyotrophic lateral sclerosisBiophysicsNuclear magnetic resonance spectroscopyProtein aggregationSuperoxide dismutaseEnergy landscapeExcited stateCrystallographyStereochemistryBiochemistryBiologyDiseaseEnzymeGeneMedicineMolecule

Abstract

fetched live from OpenAlex

and characterize their free energy landscapes as a first step in understanding the initial stages of disease progression and, more generally, to evaluate the plasticity of low-lying protein conformational states. The mutations lead to little change in the structures and dynamics of the ground states of the mutant proteins. By contrast, the numbers of low-lying excited states that are accessible to each of the disease mutants can vary significantly, with additional conformers accessed in some cases. Our study suggests that the diversity of these structures can provide alternate interaction motifs for different mutants, establishing additional pathways for new and often aberrant intra- and intermolecular contacts. Further, it emphasizes the potential importance of conformationally excited states in directing both folding and misfolding processes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.130
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.314
Teacher spread0.279 · 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

Citations61
Published2016
Admission routes2
Has abstractyes

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