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Record W2298718405 · doi:10.1021/acs.biomac.5b00567

The Interplay of Disulfide Bonds, α-Helicity, and Hydrophobic Interactions Leads to Ultrahigh Proteolytic Stability of Peptides

2015· article· en· W2298718405 on OpenAlexaff
Yaqi Chen, Chaoqiong Yang, Tao Li, Miao Zhang, Yang Liu, Marc A. Gauthier, Yibing Zhao, Chuanliu Wu

Bibliographic record

VenueBiomacromolecules · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
FundersMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsChemistryPeptideDimerRNase PCombinatorial chemistryProtein–protein interactionProteolysisNon-covalent interactionsFolding (DSP implementation)Hydrophobic effectProteolytic enzymesStereochemistryBiophysicsBiochemistryHydrogen bondMoleculeOrganic chemistryEnzymeRNABiology

Abstract

fetched live from OpenAlex

The contribution of noncovalent interactions to the stability of naturally occurring peptides and proteins has been generally acknowledged, though how these can be rationally manipulated to improve the proteolytic stability of synthetic peptides remains to be explored. In this study, a platform to enhance the proteolytic stability of peptides was developed by controllably dimerizing them into α-helical dimers, connected by two disulfide bonds. This platform not only directs peptides toward an α-helical conformation but permits control of the interfacial hydrophobic interactions between the peptides of the dimer. Using two model dimeric systems constructed from the N-terminal α-helix of RNase A and known inhibitors for the E3 ubiquitin ligase MDM2 (and its homologue MDMX), a deeper understanding into the interplay of disulfide bonds, α-helicity, and hydrophobic interactions on enhanced proteolytic stability was sought out. Results reveal that all three parameters play an important role on attaining ultrahigh proteolytic resistance, a concept that can be exploited for the development of future peptide therapeutics. The understanding gained through this study will enable this strategy to be tailored to new peptides because the proposed strategy displays substantial tolerance to sequence permutation. It thus appears promising for conveniently creating prodrugs composed entirely of the therapeutic peptide itself (i.e., in the form of a dimer).

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 score0.002
Threshold uncertainty score0.341

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 head0.013
GPT teacher head0.275
Teacher spread0.262 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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