Discovery of polyphosphate-interacting human lysine-rich proteins and their functional implications
Bibliographic record
Abstract
Inorganic polyphosphate (polyP), a conserved phosphate polymer, modulates protein function across diverse biological systems, yet its human protein interactome remains poorly defined. While prior studies identified denaturation-resistant polyP modifications on lysine-rich sequences in yeast and bacteria, such targets in human proteins were previously unknown. Here, we performed a systematic screen of 57 lysine-rich human proteins, selected via mass spectrometry and bioinformatics, identifying 41 proteins that exhibit denaturation-resistant polyP modifications. This marks the first identification of lysine-rich motifs as polyP targets in human proteins. Through mutagenesis and binding assays, we establish these lysine-rich motifs, rather than traditional PASK domains, are critical for polyP binding, with consecutive lysine residues and intrinsic disorder as key determinants. Functional assays demonstrate that polyP binding inhibits phase separation of the transcriptional regulator NKAP, suppresses K-RAS GTPase activation, and reduces DDX55 helicase activity, revealing direct regulatory mechanisms. This work not only marks the first identification of denaturation-resistant polyP modifications in human proteins but also provides a comprehensive resource for investigating polyP's roles in human cellular homeostasis and disease.
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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".