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Record W4247376798 · doi:10.3410/f.738428614.793578998

Faculty Opinions recommendation of Hidden kinetic traps in multidomain folding highlight the presence of a misfolded but functionally competent intermediate.

2020· dataset· en· W4247376798 on OpenAlexaff
Benoit Coulombe

Bibliographic record

VenueFaculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2020
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsMontreal Clinical Research Institute
FundersAssociazione Italiana per la Ricerca sul CancroSapienza Università di RomaEuropean Commission
KeywordsPDZ domainFolding (DSP implementation)Protein foldingComputational biologyProteomeTandemHuman proteome projectChemistryBiophysicsTandem repeatNative stateBiologyBiochemistryProteomicsGenomeGene

Abstract

fetched live from OpenAlex

Although more than 75% of the proteome is composed of multidomain proteins, current knowledge of protein folding is based primarily on studies of isolated domains. In this work, we describe the folding mechanism of a multidomain tandem construct comprising two distinct covalently bound PDZ domains belonging to a protein called Whirlin, a scaffolding protein of the hearing apparatus. In particular, via a synergy between NMR and kinetic experiments, we demonstrate the presence of a misfolded intermediate that competes with productive folding. In agreement with the view that tandem domain swapping is a potential source of transient misfolding, we demonstrate that such a kinetic trap retains native-like functional activity, as shown by the preserved ability to bind its physiological ligand. Thus, despite the general knowledge that protein misfolding is intimately associated with dysfunction and diseases, we provide a direct example of a functionally competent misfolded state. Remarkably, a bioinformatics analysis of the amino acidic sequence of Whirlin from different species suggests that the tendency to perform tandem domain swapping between PDZ1 and PDZ2 is highly conserved, as demonstrated by their unexpectedly high sequence identity. On the basis of these observations, we discuss on a possible physiological role of such misfolded intermediate.Copyright © 2020 the Author(s). Published by PNAS. PMID: 32747559

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0490.026

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 head0.019
GPT teacher head0.314
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueFaculty Opinions – Post-Publication Peer Review of the Biomedical LiteratureSame topicMachine Learning in BioinformaticsFrench-language works237,207