Identification of Inositol Phosphate or Phosphoinositide Interacting Proteins by Affinity Chromatography Coupled to Western Blot or Mass Spectrometry
Bibliographic record
Abstract
Inositol phosphates and phosphoinositides regulate several cellular processes in eukaryotes, including gene expression, vesicle trafficking, signal transduction, metabolism, and development. These metabolites perform this regulatory activity by binding to proteins, thereby changing protein conformation, catalytic activity, and/or interactions. The method described here uses affinity chromatography coupled to mass spectrometry or Western blotting to identify proteins that interact with inositol phosphates or phosphoinositides. Inositol phosphates or phosphoinositides are chemically tagged with biotin, which is then captured via streptavidin conjugated to agarose or magnetic beads. Proteins are isolated by their affinity of binding to the metabolite, then eluted and identified by mass spectrometry or Western blotting. The method has a simple workflow that is sensitive, non-radioactive, liposome-free, and customizable, supporting the analysis of protein and metabolite interaction with precision. This approach can be used in label-free or in amino acid-labelled quantitative mass spectrometry methods to identify protein-metabolite interactions in complex biological samples or using purified proteins. This protocol is optimized for the analysis of proteins from Trypanosoma brucei, but it can be adapted to related protozoan parasites, yeast or mammalian cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".