Photolabeling Study of the Ligand Binding Domain of Natriuretic Peptide Receptor A: Development of a Model
Bibliographic record
Abstract
Atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) are loop-shaped peptidic hormones that have multiple actions on body fluid homeostasis. Their physiological effects are mediated through the activation of their receptor, natriuretic peptide receptor A (NPRA). This receptor is a member of the membrane guanylyl cyclase family and catalyzes cyclic guanosine monophosphate (cGMP) production following its activation. To map the binding site of human NPRA, we applied the methionine proximity assay method to this receptor. We photolabeled NPRA mutants, presenting a single methionine in the binding domain of the receptor, and used benzoylphenylalanine- (Bpa-) substituted peptides at positions 0, 3, 18, 26, and 28 of the ligand. We identified that the N-terminus of the peptide is interacting with the region between Asp(177) and Val(183) of the receptor. Arg(3) is interacting in the vicinity of Phe(172). Leu(18) binds close to Val(116). Phe(26) binds in the vicinity of His(195), and the C-terminal Tyr(28) is located close to Met(173). We next proceeded with photolabeling of a dual Bpa-substituted peptide and showed that the N-terminus and Leu(18) interact with opposite receptor subunits. On the basis of our results, a molecular model of peptide-bound NPRA was developed by homology modeling with the C-type natriuretic peptide- (CNP-) bound natriuretic peptide receptor C (NPRC) crystal structure. The model has been validated by molecular dynamics simulations. Our work provides a rational basis for interpreting and predicting natriuretic peptide binding to the human NPRA.
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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 | 0.001 | 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".