Comparative study of the unbinding process of some HTLV-1 protease inhibitors using Unbiased Molecular Dynamics simulation
Bibliographic record
Abstract
Abstract The HTLV-1 protease is one of the major antiviral targets to overwhelm this virus. Several research groups have been developing protease inhibitors over the years, but none has been successful. In this regard, the development of new HTLV-1 protease inhibitors based on fixing the defects of previous inhibitors will overcome the absence of curative treatment for this oncovirus. Thus, we decided to study the unbinding pathways of the most potent (compound 10, Ki = 15 nM) and one of the weakest (compound 9, Ki = 7900 nM) protease inhibitors, which are very structurally similar, with the PDB IDs: 4YDG, 4YDF, using the Supervised Molecular Dynamics (SuMD) method. In this project, we had various short and long-time-scale simulations, that in total, we could have 12 successful unbindings (a total of 14.8 μs) for the two compounds in both mp forms. This comparative study measured all the essential factors simultaneously in two different inhibitors, which improved our results. This study revealed that Asp32 or Asp32′ in the two forms of mp state similarly exert super power effects on maintaining both potent and weak inhibitors in the binding pocket of HTLV-1 protease. In parallel with the important impact of these two residues, in the potent inhibitor’s unbinding process, His66′ was a great supporter, that was absent in the weak inhibitor’s unbinding pathway. In contrast, in the weak inhibitor’s unbinding process, Trp98/Trp98′ by pi-pi stacking interactions were unfavorable for the stability of the inhibitor in the binding site. In our opinion, these results will assist in designing more potent and effective inhibitors for the HTLV-1 protease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".