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Record W4387473226 · doi:10.1002/anie.202310115

Precise Recognition in Water by an <i>Endo</i>‐Functionalized Cavity: Tuning the Complementarity of Binding Sites

2023· article· en· W4387473226 on OpenAlexaff
Yanfang Wang, Lili Wang, Xiaobin Zhang, Hao Nian, Li‐Shuo Zheng, Xiaoping Wang, Georg Schreckenbach, Wei Jiang, Liu‐Pan Yang

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsComplementarity (molecular biology)Hydrogen bondMolecular recognitionSelectivityBinding siteBinding selectivityChemistryDensity functional theoryHydrophobic effectCombinatorial chemistryChemical physicsMoleculeComputational chemistryBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Precise binding towards structurally similar substrates is a common feature of biomolecular recognition. However, achieving such selectivity-especially in distinguishing subtle differences in substrates-with synthetic hosts can be quite challenging. Herein, we report a novel design strategy involving the combination of different rigid skeletons to adjust the distance between recognition sites within the cavity, which allows for the highly selective recognition of hydrogen-bonding complementary substrates, such as 4-chromanone. X-ray single-crystal structures and density functional theory calculations confirmed that the distance of endo-functionalized groups within the rigid cavity is crucial for achieving high binding selectivity through hydrogen bonding. The thermodynamic data and molecular dynamics simulations revealed a significant influence of the hydrophobic cavity on the binding affinity. The new receptor possesses both high selectivity and high affinity, which provide valuable insights for the design of customized receptors.

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 score0.020
Threshold uncertainty score0.590

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 head0.031
GPT teacher head0.281
Teacher spread0.250 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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