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Record W2898163614 · doi:10.1002/prot.25622

Solution NMR structure of CHU_1110 from <i>Cytophaga hutchinsonii</i>, an AHSA1 protein potentially involved in metal ion stress response

2018· article· en· W2898163614 on OpenAlexaff
Chunjie Liang, Jiang Zhu, Theresa A. Ramelot, Michael A. Kennedy, Xiali Yue, Xuegang Li, Maili Liu, Ting He, Yunhuang Yang

Bibliographic record

VenueProteins Structure Function and Bioinformatics · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsStructural Genomics Consortium
FundersNational Institute of General Medical SciencesNational Natural Science Foundation of China
KeywordsAntiparallel (mathematics)ChemistryContext (archaeology)Metal ions in aqueous solutionCrystallographyCytophagaMetalBiochemistryBacteriaStereochemistryBiologyGeneticsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

We report the solution nuclear magnetic resonance (NMR) structure of CHU_1110 from Cytophaga hutchinsonii. CHU_1110 contains three α-helices and one antiparallel β-sheet, forming a large cavity in the center of the protein, which are consistent with the structural characteristics of AHSA1 protein family. This protein shows high structural similarities to the prokaryotic proteins RHE_CH02687 from Rhizobium etli and YndB from Bacillus subtilis, which can bind with flavinoids. Unlike these two homologs, CHU_1110 shows no obvious interaction with flavonoids in NMR titration experiments. In addition, no direct interaction has been observed between CHU_1110 and ATP, although many homologous sequences of CHU_1110 have been annotated as ATPase. Combining the analysis of structural similarity of CHU_1110 and genomic context of its encoding gene, we speculate that CHU_1110 may be involved in the stress response of bacteria to heavy metal ions, even though its specific biological functions that need to be further investigated.

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 score0.080
Threshold uncertainty score0.929

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)0.0000.000

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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