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Record W3105514549 · doi:10.1101/2020.11.18.389114

Molecular basis for inhibition of adhesin-mediated bacterial-host interactions through a novel peptide-binding domain

2020· preprint· en· W3105514549 on OpenAlexafffund
Shuaiqi Guo, Hossein Shahbani Zahiri, Corey A. Stevens, Daniel C. Spaanderman, L.‐G. Milroy, Christian Ottmann, Luc Brunsveld, Ilja K. Voets, Peter L. Davies

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Structural Characterization
Canadian institutionsQueen's University
FundersDeutsches Elektronen-SynchrotronQueen's UniversityHanyang UniversityCanadian Light Source
KeywordsBacterial adhesinPeptideBiofilmLigand (biochemistry)AffinitiesBacteriaBiochemistryBiologyChemistryPlasma protein bindingCell biologyComputational biologyReceptorGeneticsVirulenceGene

Abstract

fetched live from OpenAlex

Abstract Modulation of protein-protein interactions (PPIs) with small-molecules is a promising conceptual approach in drug discovery. In the area of bacterial colonization, PPIs contribute to adhesin-mediated biofilm formation that cause most infections. However, the molecular basis underlying these adhesin-ligand interactions is largely unknown. The 1.5-MDa adhesion protein, MpIBP, uses a peptide-binding domain ( Mp PBD) to help its Antarctic bacterium form symbiotic biofilms on sea ice with microalgae such as diatoms. X-ray crystallography revealed Mp PBD uses Camdependent interactions to self-associate with a crystal symmetry mate via the C-terminal threonine-proline-aspartate sequence. Structure-guided optimization derived penta-peptide ligands that bound Mp PBD 1,000-fold more tightly, with affinities in the nano-molar range. These ligands act as potent antagonists to block Mp PBD from binding to the diatom cells. Since adhesins of some human pathogens contain peptide-binding module homologs of Mp PBD, this same conceptual approach could help develop ligand-based PPI modulators to disrupt harmful bacteria-host interactions.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

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 integrity0.0010.000
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 head0.015
GPT teacher head0.228
Teacher spread0.213 · 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.

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
Published2020
Admission routes2
Has abstractyes

Explore more

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