Network of long‐range concerted chemical shift displacements upon ligand binding to human angiogenin
Bibliographic record
Abstract
Molecular recognition models of both induced fit and conformational selection rely on coupled networks of flexible residues and/or structural rearrangements to promote protein function. While the atomic details of these motional events still remain elusive, members of the pancreatic ribonuclease superfamily were previously shown to depend on subtle conformational heterogeneity for optimal catalytic function. Human angiogenin, a structural homologue of bovine pancreatic RNase A, induces blood vessel formation and relies on a weak yet functionally mandatory ribonucleolytic activity to promote neovascularization. Here, we use the NMR chemical shift projection analysis (CHESPA) to clarify the mechanism of ligand binding in human angiogenin, further providing information on long-range intramolecular residue networks potentially involved in the function of this enzyme. We identify two main clusters of residue networks displaying correlated linear chemical shift trajectories upon binding of substrate fragments to the purine- and pyrimidine-specific subsites of the catalytic cleft. A large correlated residue network clusters in the region corresponding to the V1 domain, a site generally associated with the angiogenic response and structural stability of the enzyme. Another correlated network (residues 40-42) negatively affects the catalytic activity but also increases the angiogenic activity. (15) N-CPMG relaxation dispersion experiments could not reveal the existence of millisecond timescale conformational exchange in this enzyme, a lack of flexibility supported by the very low-binding affinities and catalytic activity of angiogenin. Altogether, the current report potentially highlights the existence of long-range dynamic reorganization of the structure upon distinct subsite binding events in human angiogenin.
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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 | 0.000 |
| 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".