Binding affinities for sulfonamide inhibitors with matrix metalloproteinase‐2 using a linear response method
Bibliographic record
Abstract
Due to their involvement in many pathological conditions, matrix metalloproteinases (MMPs), are very attractive therapeutic targets. Our study focuses on one of them, MMP-2, which is involved in tumor progression and metastasis. Recently, the solution structure of the catalytic domain of MMP-2 complexed with a hydroxamic acid inhibitor (SC-74020) was published by Feng et al. Using the Hanessian group published binding affinity data and the structure published by Feng as a basis, we have built a binding affinity model by targeting the S(2)' pocket of the enzyme with a set of nine alpha-N-sulfonylamino hydroxamic acid derivatives. Two binding geometries of each ligand have been generated corresponding to two binding modes denoted A and B, respectively, of which the first one is targeting the S(2)' pocket and the second one the S(1) pocket. For the binding affinity model developed for mode A the computed activities show a rmsd of 0.583 kcal/mol as compared with the experimental data, and a correlation coefficient r(2) of 0.779, while in the case of the binding mode B a rmsd of 0.834 kcal/mol and correlation coefficient r(2) of 0.500, respectively, were obtained. In conclusion, our data suggest a higher probability for the Phe(76) gated S(2)' open form pocket to accommodate the substituent alpha versus the wide solvent exposed S(1) subsite, probability which some research groups could have overlooked due to extensive use in their calculations of non revealing S(2)' pocket open state crystallographic structures instead of NMR ones.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".