Using hydrogen deuterium exchange mass spectrometry to engineer optimized constructs for crystallization of protein complexes: Case study of PI4KIIIβ with Rab11
Bibliographic record
Abstract
The ability of proteins to bind and interact with protein partners plays fundamental roles in many cellular contexts. X-ray crystallography has been a powerful approach to understand protein-protein interactions; however, a challenge in the crystallization of proteins and their complexes is the presence of intrinsically disordered regions. In this article, we describe an application of hydrogen deuterium exchange mass spectrometry (HDX-MS) to identify dynamic regions within type III phosphatidylinositol 4 kinase beta (PI4KIIIβ) in complex with the GTPase Rab11. This information was then used to design deletions that allowed for the production of diffraction quality crystals. Importantly, we also used HDX-MS to verify that the new construct was properly folded, consistent with it being catalytically and functionally active. Structures of PI4KIIIβ in an Apo state and bound to the potent inhibitor BQR695 in complex with both GTPγS and GDP loaded Rab11 were determined. This hybrid HDX-MS/crystallographic strategy revealed novel aspects of the PI4KIIIβ-Rab11 complex, as well as the molecular mechanism of potency of a PI4K specific inhibitor (BQR695). This approach is widely applicable to protein-protein complexes, and is an excellent strategy to optimize constructs for high-resolution structural approaches.
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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 | 0.001 |
| 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 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".