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Record W3213182364 · doi:10.1002/cbic.202100502

Binding Methylarginines and Methyllysines as Free Amino Acids: A Comparative Study of Multiple Host Classes**

2021· article· en· W3213182364 on OpenAlexafffund
Zoey Warmerdam, Bianca Kamba, My‐Hue Le, Thomas Schräder, Lyle Isaacs, Peter Bayer, Fraser Hof

Bibliographic record

VenueChemBioChem · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Victoria
FundersDivision of ChemistryNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftNational Science Foundation
KeywordsCucurbiturilBinding affinitiesAmino acidAffinitiesChemistryBinding selectivityHost (biology)StereochemistrySelectivityMethylationBiochemistryBiologyOrganic chemistrySupramolecular chemistryMoleculeDNAGenetics

Abstract

fetched live from OpenAlex

Methylated free amino acids are an important class of targets for host-guest chemistry that have recognition properties distinct from those of methylated peptides and proteins. We present comparative binding studies for three different host classes that are each studied with multiple methylated arginines and lysines to determine fundamental structure-function relationships. The hosts studied are all anionic and include three calixarenes, two acyclic cucurbiturils, and two other cleft-like hosts, a clip and a tweezer. We determined the binding association constants for a panel of methylated amino acids using indicator displacement assays. The acyclic cucurbiturils display stronger binding to the methylated amino acids, and some unique patterns of selectivity. The two other cleft-like hosts follow two different trends, shallow host (clip) following similar trends to the calixarenes, and the other more closed host (tweezer) binding certain less-methylated amino acids stronger than their methylated counterparts. Molecular modelling sheds some light on the different preferences of the various hosts. The results identify hosts with new selectivities and with affinities in a range that could be useful for biomedical applications. The overall selectivity patterns are explained by a common framework that considers the geometry, depth of binding pockets, and functional group participation across all host classes.

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)0.001
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 score0.012
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.023
GPT teacher head0.288
Teacher spread0.265 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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