Inositol phosphate‐induced stabilization of inositol 1,3,4,5,6‐pentakisphosphate 2‐kinase and its role in substrate specificity
Bibliographic record
Abstract
Inositol phosphate kinases (IPKs) sequentially phosphorylate inositol phosphates (IPs) on their inositol rings to yield an array of signaling molecules. IPKs must possess the ability to recognize their physiological substrates from among a pool of over 30 cellular IPs that differ in numbers and positions of phosphates. Crystal structures from IPK subfamilies have revealed structural determinants for IP discrimination, which vary considerably between IPKs. However, recent structures of inositol 1,3,4,5,6-pentakisphosphate 2-kinase (IPK1) did not reveal how IPK1 selectively recognizes its physiological substrate, IP5, while excluding others. Here, we report that limited proteolysis has revealed the presence of multiple conformational states in the IPK1 catalytic cycle, with notable protection from protease only in the presence of IP. Further, a 3.1-Å crystal structure of IPK1 bound to ADP in the absence of IP revealed decreased order in residues 110-140 within the N-lobe of the kinase compared with structures in which IP is bound. Using this solution and crystallographic data, we propose a model for recognition of IP substrate by IPK1 wherein phosphate groups at the 4-, 5-, and 6-positions are recognized initially by the C-lobe with subsequent interaction of the 1-position phosphate by Arg130 that stabilizes this residue and the N-lobe. This model explains how IPK1 can be highly specific for a single IP substrate by linking its interactions with substrate phosphate groups to the stabilization of the N- and C-lobes and kinase activation.
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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".