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Thiophosphate Bioisosteres of Inositol Hexakisphosphate Enhances Binding Affinity and Residence Time to Bacterial Virulence Factors.

2024· preprint· en· W4402814327 on OpenAlexaff
Rebecca Cummer, Garvit Bhatt, Lauren M. Finn, Bettina G. Keller, Bhushan Nagar, Bastien Castagner

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPhytase and its Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsThiophosphateInositolResidenceVirulenceChemistryStereochemistryBiochemistryReceptorSociologyOrganic chemistryDemography

Abstract

fetched live from OpenAlex

Inositol phosphates are essential for mammalian cell signalling with critical roles in cellular processes. The fully phosphorylated inositol phosphate, myo-inositol hexakisphosphate (IP6), modulates numerous eukaryotic proteins and bacterial virulence factors. It has been suggested that the high charge density of IP6 causes restructuring of virulence factors in mammalian cells, activating their enzymatic activity. IP6 is challenging to study due to its phytase instability, hydrophilicity, and propensity to precipitate. Here we suggest that the thiophosphate bioisostere, myo-inositol hexakisthiophosphate (IT6) will mitigate these issues, as thiophosphate substitution has been found to be phytase resistant and improve solubility. Assessment of the chemical properties of IT6 has indeed validated these characteristics. In addition, we performed biophysical characterization of IT6 binding to the virulence factors Salmonella enterica serovar Typhimurium AvrA, Vibrio parahaemolyticus VopA, and Clostridioides difficile TcdB. Our data show that the higher charge density of IT6 increased its binding affinity and residence time to the proteins, which improved stabilization of the bound-state. IT6 is a valuable tool for structural biology research and the described biophysical characteristics of thiophosphate substitution is of value in medicinal chemistry.

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 score0.021
Threshold uncertainty score0.651

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 science0.0000.001
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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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