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Record W4396900139 · doi:10.3389/fnmol.2024.1386219

Small but mighty: the rise of microprotein biology in neuroscience

2024· article· en· W4396900139 on OpenAlexaff
Erin E. Duffy, Elena G. Assad, Brian T. Kalish, Michael E. Greenberg

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsMental Health Research CanadaHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentDamon Runyon Cancer Research Foundation
KeywordsBiologyNeuroscienceORFSOpen reading frameComputational biologyBrain functionFunction (biology)Systems biologyGeneticsGenePeptide sequence

Abstract

fetched live from OpenAlex

The mammalian central nervous system coordinates a network of signaling pathways and cellular interactions, which enable a myriad of complex cognitive and physiological functions. While traditional efforts to understand the molecular basis of brain function have focused on well-characterized proteins, recent advances in high-throughput translatome profiling have revealed a staggering number of proteins translated from non-canonical open reading frames (ncORFs) such as 5' and 3' untranslated regions of annotated proteins, out-of-frame internal ORFs, and previously annotated non-coding RNAs. Of note, microproteins < 100 amino acids (AA) that are translated from such ncORFs have often been neglected due to computational and biochemical challenges. Thousands of putative microproteins have been identified in cell lines and tissues including the brain, with some serving critical biological functions. In this perspective, we highlight the recent discovery of microproteins in the brain and describe several hypotheses that have emerged concerning microprotein function in the developing and mature nervous system.

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)0.000
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 score0.165
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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