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Record W4398164813 · doi:10.1021/acs.jmedchem.4c00005

Secondary Structure Stabilization of Macrocyclic Antimicrobial Peptides via Cross-Link Swapping

2024· article· en· W4398164813 on OpenAlexafffund
Nauman Nazeer, Navjote Kooner, Anupama Ghimire, Jan K. Rainey, William D. Lubell, Deniz Meneksedag‐Erol, Marya Ahmed

Bibliographic record

VenueJournal of Medicinal Chemistry · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsDalhousie UniversityUniversité de MontréalConcordia UniversityUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsChemistryAntimicrobialLink (geometry)Combinatorial chemistryCross-linkAntimicrobial peptidesStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Lactam cross-links have been employed to stabilize the helical secondary structure and enhance the activity and physiological stability of antimicrobial peptides; however, stabilization of β-sheets via lactamization has not been observed. In the present study, lactams between the side chains of C- and N-terminal residues have been used to stabilize the β-sheet conformation in a short ten-residue analogue of chicken angiogenin-4. Designed using a combination of molecular dynamics simulations and Markov state models, the lactam cross-linked peptides are shown to adopt stabilized β-sheet conformations consistent with simulated structures. Replacement of the peptide side-chain Cys-Cys disulfide by a lactam cross-link enhanced the broad-spectrum antibacterial activity compared to the parent peptide and exhibited greater propensity to induce proinflammatory activity in macrophages. The combination of molecular simulations and conformational and biological analyses of the synthetic peptides provides a useful paradigm for the rational design of therapeutically active peptides with constrained β-sheet structures.

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 score0.006
Threshold uncertainty score0.466

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)0.0000.000

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 head0.004
GPT teacher head0.254
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

Citations13
Published2024
Admission routes2
Has abstractyes

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