Structure‐based statistical modeling and analysis of peptide affinity and cross‐reactivity to human senile osteoporosis <scp>OSF SH3</scp> domain
Bibliographic record
Abstract
Abstract Human osteoclast‐stimulating factor (OSF) induces osteoclast formation and bone resorption in senile osteoporosis by recruiting multiple signaling complexes with cognate interacting partners through its N‐terminal Src homology 3 (SH3) peptide‐recognition domain. The domain can recognize and bind to the polyproline regions of its partner proteins, rendering a broad ligand specificity and cross‐reactivity. Here, the structural basis and physicochemical property of peptide affinity and cross‐reactivity to OSF SH3 domain were investigated systematically by using an integration of statistical analysis and molecular modeling. A structure‐based quantitative structure‐activity relationship method called cross‐nonbonded interaction characterization and statistical regression was used to characterize the intermolecular interactions involved in computationally modeled domain‐peptide complex structures and then to correlate the interactions with affinity for a panel of collected SH3‐binding peptide samples. Both the structural stability and generalization ability of obtained quantitative structure‐activity relationship regression models were examined rigorously via internal cross‐validation and external test, confirming that the models can properly describe even single‐residue mutations at domain‐peptide complex interface and give a reasonable extrapolation for the mutation effect on peptide affinity. Subsequently, the best model was used to investigate the promiscuity and cross‐reactivity of OSF SH3 domain binding to its various peptide ligands. It is found that few key residues in peptide ligands are primarily responsible for the domain affinity and selectivity, while most other residues only play a minor role in domain‐peptide binding affinity and stability. The peptide residues can be classified into 3 groups in terms of their contribution to ligand selectivity: key, assistant, and marginal residues. Considering that the key residues are very few so that many domain interacting partners share a similar binding profile, additional factors such as in vivo environments and biological contexts would also contribute to the specificity and cross‐reactivity of OSF SH3 domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".