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Record W2028131660 · doi:10.1021/pr0704027

Local and Nonlocal Environments around Cis Peptides

2007· article· en· W2028131660 on OpenAlexaff
Brent Wathen, Zongchao Jia

Bibliographic record

VenueJournal of Proteome Research · 2007
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsChemistryBiophysicsComputational biologyBiology

Abstract

fetched live from OpenAlex

Although the vast majority of peptide bonds in folded proteins are found in the trans conformation, a small percentage are found in the less energetically favorable cis conformation. Though the mechanism of cis peptide bond formation remains unknown, the role of local aromatics has been emphasized in the literature. This paper presents results from a comprehensive statistical analysis of both the local and nonlocal (i.e., tertiary) environment around cis peptides. In addition to an increased frequency of aromatic residues in the local environment around cis peptides, a number of nonlocal differences in protein secondary and tertiary structure between cis and trans peptides are found: (i) coil regions containing cis peptides are almost twice as long as those without cis peptides and include more Tyr and Pro residues; (ii) cis peptides occur with high frequencies in coil regions near large beta-structures; (iii) there is a nonlocal enrichment of Cys, His, Tyr, and Ser in the tertiary environment surrounding cis peptides when compared to trans peptides; and (iv) on average, cis peptides make fewer medium-range and more long-range contacts than trans peptides do. On the basis of these observations, it is concluded that nonlocal factors play a significant role in cis peptide formation, which has not been fully appreciated previously. An autocatalytic model for cis peptide formation is discussed as are consequences for protein folding.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.416
Teacher spread0.338 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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