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Record W2895946932 · doi:10.1021/acs.jpcb.8b07680

Prediction of Misfolding-Specific Epitopes in SOD1 Using Collective Coordinates

2018· article· en· W2895946932 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsGenome British ColumbiaUniversity of British Columbia
FundersAlberta Prion Research InstituteCanadian Institutes of Health Research
KeywordsEpitopeSOD1Protein foldingComputational biologyComputer scienceBiologyCell biologyImmunologyGenetics

Abstract

fetched live from OpenAlex

We introduce a global, collective coordinate bias into molecular dynamics simulations that partially unfolds a protein, in order to predict misfolding-specific epitopes based on the regions that locally unfold. Several metrics are used to measure local disorder, including solvent exposed surface area (SASA), native contacts ( Q), and root mean squared fluctuations (RMSF). The method is applied to Cu, Zn superoxide dismutase (SOD1). For this protein, the processes of monomerization, metal loss, and conformational unfolding due to microenvironmental stresses are all separately taken into account. Several misfolding-specific epitopes are predicted, and consensus epitopes are calculated. These predicted epitopes are consistent with the "lower-resolution" peptide sequences used to raise disease-specific antibodies, but the epitopes derived from collective coordinates contain shorter, more refined sequences for the key residues constituting the epitope.

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.

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 score0.006
Threshold uncertainty score0.263

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 head0.016
GPT teacher head0.261
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