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Record W3103413608 · doi:10.1101/2020.05.18.103002

Protein context shapes the specificity of domain-peptide interactions <i>in vivo</i>

2020· preprint· en· W3103413608 on OpenAlexafffund
Ugo Dionne, Émilie Bourgault, Alexandre K. Dubé, David Bradley, François Chartier, Rohan Dandage, Soham Dibyachintan, Philippe C Després, Gerald Gish, Jean‐Philippe Lambert, Nicolas Bisson, Christian R. Landry

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsMount Sinai HospitalUniversité LavalLunenfeld-Tanenbaum Research InstitutePROTEO
FundersCentre Hospitalier Universitaire de QuébecNatural Sciences and Engineering Research Council of CanadaMitacsCanadian Institutes of Health ResearchUniversité Laval
KeywordsSH3 domainComputational biologyContext (archaeology)Homology (biology)BiologyProtein domainPeptideProtein–protein interactionBinding selectivityProto-oncogene tyrosine-protein kinase SrcCell biologyGeneticsAmino acidBiochemistryPhosphorylationGene

Abstract

fetched live from OpenAlex

Abstract Protein-protein interactions (PPIs) between modular binding domains and their target peptide motifs are thought to largely depend on the intrinsic binding specificities of the domains. By combining deletion, mutation, swapping and shuffling of SRC Homology 3 (SH3) domains and measuring their impact on protein interactions, we find that most SH3s do not autonomously dictate PPI specificity in vivo . The identity of the host protein and the position of the SH3 domains within their host are both critical for PPI specificity, for cellular functions and for key biophysical processes such as phase separation. Our work demonstrates the importance of the interplay between a modular PPI domain such as SH3 and its host protein in establishing specificity to wire PPI networks.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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