Novel Allosteric Pathway of Eg5 Regulation Identified through Multivariate Statistical Analysis of Hydrogen-Exchange Mass Spectrometry (HX-MS) Ligand Screening Data
Bibliographic record
Abstract
The mitotic kinesin Eg5 is an important target in cancer chemotherapy. A structurally diverse collection of canonical loop L5 inhibitors engage an allosteric pathway that includes elements of its microtubule binding region. However, recent evidence suggests that Eg5 may permit alternative allosteric mechanisms. Terpendole E, a natural-product Eg5 inhibitor, is active against mutants resistant to canonical loop L5 inhibitors and appears to offer a unique mode of inhibition. To investigate the variety of inhibitor responses, the structure-function properties of eighteen kinesin inhibitors were quantified with hydrogen-exchange mass spectrometry (HX-MS), functional analysis and molecular modeling. A unique strategy for high-density data analysis was implemented, based on a scalable multivariate statistical method, as current HX-MS routines have a limited capacity to guide a characterization of ligands when additional functional data is available. Inhibitor evaluation was achieved using orthogonal partial least squares projection to latent structures discriminant analysis (OPLS-DA). The strategy generated a model that identified functionally-significant conformational elements involved in kinesin inhibition, confirming the canonical allosteric pathway and identifying a novel response pathway. Terpendole E is demonstrated to be an atypical L5 site inhibitor, where binding induces an allosteric effect mediated by a destabilization in the β-sheet core of the molecular motor, an element involved in mechanochemical coupling for structurally-related kinesins. The analysis suggests that a different approach to inhibitor development may be fruitful.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".