Hydrolysis Mechanism of the Linkers by Matrix Metalloproteinase-9 Using QM/MM Calculations
Bibliographic record
Abstract
Activatable cell-penetrating peptides (ACPPs) are known to be able to decrease the cytotoxicity of cell-penetrating peptide (CPP)-based drug delivery systems. Furthermore, they can improve the targeting of CPPs when specifically recognized and hydrolyzed by characteristic proteases. A comprehensive and profound understanding of the recognition and hydrolysis process will provide a better design of the ACPP-based drug delivery system. Previous studies have clearly described how ACPPs are recognized and bound by MMPs. However, the hydrolysis mechanism of ACPPs is still unsolved. This work focuses on a proteinase-sensitive cleavable linker of ACPPs (PLGLAG), the key structure for recognition and hydrolysis, trying to determine the mechanism by which MMP-9 hydrolyzes its substrate PLGLAG. The quantum mechanics/molecular mechanics (QM/MM) calculations herein show that MMP-9 proteolysis is a water-mediated four-step reaction. More specifically, it consists of (i) nucleophilic attack, (ii) hydrogen-bond rearrangement, (iii) proton transfer, and finally (iv) amide bond rupture. Considering the reversibility of multistep reaction, the second step (i.e., hydrogen-bond rearrangement) has the highest barrier and is the rate-limiting step in the hydrolysis of PLGLAG. The possible design and improvement of the key P1 and P1' sites are also explored through mutations. The present results indicate that, while the mutations affect the reaction energy barriers and the rate-limiting steps, all mutants considered could be hydrolyzed by MMP-9. To provide further insights, the hydrolysis mechanism of MMP-2, which has a similar hydrolysis process to that of MMP-9 but with different reaction barriers, is also studied and compared. As a result, this work provides detailed insights into the hydrolysis mechanism of ACPPs by MMP-9 and, thus, also possible insights for the development of new strategies for ACPP-based delivery systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".