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Record W2509545693 · doi:10.1038/srep31817

Staphylokinase has distinct modes of interaction with antimicrobial peptides, modulating its plasminogen-activation properties

2016· article· en· W2509545693 on OpenAlexafffund
Leonard T. Nguyen, Hans J. Vogel

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsStaphylokinaseMelittinAntimicrobial peptidesPlasminogen activatorDocking (animal)ChemistryAntimicrobialActivator (genetics)BiochemistryBiologyRecombinant DNAMicrobiologyPeptideGeneticsReceptorGene

Abstract

fetched live from OpenAlex

Staphylokinase (Sak) is a plasminogen activator protein that is secreted by many Staphylococcus aureus strains. Sak also offers protection by binding and inhibiting specific antimicrobial peptides (AMPs). Here, we evaluate Sak as a more general interaction partner for AMPs. Studies with melittin, mCRAMP, tritrpticin and bovine lactoferricin indicate that the truncation of the first ten residues of Sak (SakΔN10), which occurs in vivo and uncovers important residues in a bulge region, improves its affinity for AMPs. Melittin and mCRAMP have a lower affinity for SakΔN10, and in docking studies, they bind to the N-terminal segment and bulge region of SakΔN10. By comparison, lactoferricin and tritrpticin form moderately high affinity 1:1 complexes with SakΔN10 and their cationic residues form several electrostatic interactions with the protein's α-helix. Overall, our work identifies two distinct AMP binding surfaces on SakΔN10 whose occupation would lead to either inhibition or promotion of its plasminogen activating properties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.030
GPT teacher head0.222
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2016
Admission routes2
Has abstractyes

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