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Record W1997525701 · doi:10.1074/jbc.m605316200

Structural and Mutational Analysis of Human Ad37 and Canine Adenovirus 2 Fiber Heads in Complex with the D1 Domain of Coxsackie and Adenovirus Receptor

2006· article· en· W1997525701 on OpenAlexaff
Elena Seiradake, Hugues Lortat‐Jacob, Olivier Billet, Eric J. Kremer, S. Cusack

Bibliographic record

VenueJournal of Biological Chemistry · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsCanadian Nautical Research Society
FundersInstitut National de la Santé et de la Recherche Médicale
KeywordsAdenovirus infectionVirologyMastadenovirusDomain (mathematical analysis)BiologyAdenoviridaeComputational biologyGeneticsGeneRecombinant DNAVirusMathematics

Abstract

fetched live from OpenAlex

Adenovirus fibers from most serotypes bind the D1 domain of coxsackie and adenovirus receptor (CAR), although the binding residues are not strictly conserved. To understand this further, we determined the crystal structures of canine adenovirus serotype 2 (CAV-2) and the human adenovirus serotype 37 (HAd37) in complex with human CAR D1 at 2.3 and 1.5A resolution, respectively. Structure comparison with the HAd12 fiber head-CAR D1 complex showed that the overall topology of the interaction is conserved but that the interfaces differ in number and identity of interacting residues, shape complementarity, and degree of conformational adaptation. Using surface plasmon resonance, we characterized the binding affinity to CAR D1 of wild type and mutant CAV-2 and HAd37 fiber heads. We found that CAV-2 has the highest affinity but fewest direct interactions, with the reverse being true for HAd37. Moreover, we found that conserved interactions can have a minor contribution, whereas serotype-specific interactions can be essential. These results are discussed in the light of virus evolution and design of adenovirus vectors for gene transfer.

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 score0.243
Threshold uncertainty score0.306

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 studies0.0000.001
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 head0.023
GPT teacher head0.296
Teacher spread0.273 · 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

Citations92
Published2006
Admission routes1
Has abstractyes

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