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Record W4387503758 · doi:10.1016/j.jbc.2023.105336

Structural basis of aggregate binding by the AAA+ disaggregase ClpG

2023· article· en· W4387503758 on OpenAlexfundno aff
Panagiotis Katikaridis, Bernd Simon, Timo Jenne, Seongjoon Moon, Changhan Lee, Janosch Hennig, Axel Mogk

Bibliographic record

VenueJournal of Biological Chemistry · 2023
Typearticle
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsnot available
FundersNational Research Foundation of KoreaMinistry of Science, ICT and Future PlanningMinistry of EducationHeidelberg Biosciences International Graduate SchoolAjou UniversityDeutsche ForschungsgemeinschaftNational Research FoundationEuropean Molecular Biology LaboratoryCHEO Research InstituteMinistry of Science and ICT, South KoreaUniversität Heidelberg
KeywordsAggregate (composite)BiophysicsChemistryBiologyMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

Severe heat stress causes massive loss of essential proteins by aggregation, necessitating a cellular activity that rescues aggregated proteins. This activity is executed by ATP-dependent, ring-forming, hexameric AAA+ disaggregases. Little is known about the recognition principles of stress-induced protein aggregates. How can disaggregases specifically target aggregated proteins, while avoiding binding to soluble non-native proteins? Here, we determined by NMR spectroscopy the core structure of the aggregate-targeting N1 domain of the bacterial AAA+ disaggregase ClpG, which confers extreme heat resistance to bacteria. N1 harbors a Zn 2+ -coordination site that is crucial for structural integrity and disaggregase functionality. We found that conserved hydrophobic N1 residues located on a β-strand are crucial for aggregate targeting and disaggregation activity. Analysis of mixed hexamers consisting of full-length and N1-truncated subunits revealed that a minimal number of four N1 domains must be present in a AAA+ ring for high-disaggregation activity. We suggest that multiple N1 domains increase substrate affinity through avidity effects. These findings define the recognition principle of a protein aggregate by a disaggregase, involving simultaneous contacts with multiple hydrophobic substrate patches located in close vicinity on an aggregate surface. This binding mode ensures selectivity for aggregated proteins while sparing soluble, non-native protein structures from disaggregase activity.

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 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 score0.003
Threshold uncertainty score0.788

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)0.0010.000

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 head0.026
GPT teacher head0.258
Teacher spread0.232 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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