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Record W2163221461 · doi:10.1042/bst20120160

Evolutionary selection for protein aggregation

2012· review· en· W2163221461 on OpenAlexafffund
Natalia Sánchez de Groot, Marc Torrent, Anna Villar‐Piqué, Benjamin Lang, Salvador Ventura, Jörg Gsponer, M. Madan Babu

Bibliographic record

VenueBiochemical Society Transactions · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsProtein aggregationProtein foldingProteomeProtein functionFunction (biology)Human proteome projectBiologyComputational biologyChemistryCell biologyBioinformaticsGeneticsProteomicsGene

Abstract

fetched live from OpenAlex

Protein aggregation is being found to be associated with an increasing number of human diseases. Aggregation can lead to a loss of function (lack of active protein) or to a toxic gain of function (cytotoxicity associated with protein aggregates). Although potentially harmful, protein sequences predisposed to aggregation seem to be ubiquitous in all kingdoms of life, which suggests an evolutionary advantage to having such segments in polypeptide sequences. In fact, aggregation-prone segments are essential for protein folding and for mediating certain protein-protein interactions. Moreover, cells use protein aggregates for a wide range of functions. Against this background, life has adapted to tolerate the presence of potentially dangerous aggregation-prone sequences by constraining and counteracting the aggregation process. In the present review, we summarize the current knowledge of the advantages associated with aggregation-prone stretches in proteomes and the strategies that cellular systems have developed to control the aggregation process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.295
Teacher spread0.270 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations41
Published2012
Admission routes2
Has abstractyes

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