Conformational studies of vasopressin and mesotocin using NMR spectroscopy and molecular modelling methods. Part II: studies in the SDS micelle
Bibliographic record
Abstract
There is much evidence to support the hypothesis that lipids play a role in the interaction of peptide hormones with their membrane receptors. This interaction through change of peptide conformation can facilitate the entry of the hormone into the microenvironment of the receptor. In the present study we have examined the interaction of vasopressin and mesotocin with a lipid-sodium dodecylsulfate (SDS) micelle-using 2D nuclear magnetic resonance (NMR) and theoretical methods. Solution structures of two hormones in solution with SDS were established using the nuclear Overhauser effect (NOE) and the (3)J(NHHalpha) couplings. The amino acid sequences of these peptides are: c[C(1)-Y(2)-F(3)-Q(4)-N(5)-C(6)]-P(7)-R(8)-G(9)-NH(2) ([Arg(8)]vasopressin, AVP) and c[C(1)-Y(2)-I(3)-Q(4)-N(5)-C(6)]-P(7)-I(8)-G(9)-NH(2) (MT). Each of the peptides was found to occur as one stable conformation. AVP adopts the cis configuration on the Cys(1)-Tyr(2) peptide bond, a finding not reported so far. The three-dimensional structures of the two peptides studied were determined by a method that consisted of time-averaged molecular dynamics in an explicit SDS micelle with the parm99 force field in AMBER8.0 package. All calculated structures of the studied peptides form beta-turns in their cyclic parts. The C-terminal fragment of MT is bent, whereas that of AVP is extended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".