Unraveling the Binding Mode of TSC2–Rheb through Protein Docking and Simulations
Bibliographic record
Abstract
Proteasome inhibitors (PIs) constitute the first line of therapy for multiple myeloma (MM). Despite the impressive clinical efficacy, MM remains fatal due to the development of drug resistance over time. During MM progression, stress responses to hypoxia and PIs suppress mammalian target of rapamycin complex 1 (mTORC1) activity by releasing tuberous sclerosis complex 2 (TSC2), which deactivates Ras homologue enriched in brain (Rheb), a crucial regulator of mTORC1. The efficacy of PIs targeting MM is enhanced when mTORC1 is hyperactivated. We thus propose that the inhibition of TSC2 will improve the efficacy of PIs targeting MM. To the best of our knowledge, no cocrystallized structure of the TSC2-Rheb complex has been reported. We therefore developed a representative model using the individual structures of TSC2 (PDB: 7DL2) and Rheb (PDB: 1XTS). Computational modeling involving an extensive protein-protein docking consensus approach was performed to determine the putative binding mode of TSC2-Rheb. The proposed docking poses were refined, clustered, and evaluated by MD simulations to explore the conformational dynamics and protein mobility, particularly at the drug-binding interface of TSC2-Rheb. Our results agree with the suggested binding mode of TSC2-Rheb previously reported in the literature. The results reported herein establish a basis for the development of new inhibitors blocking the binding of TSC2 and Rheb, aiming to reinstate mTORC1 activation and facilitate improved efficacy of PIs against multiple myeloma.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".