Large-scale Identification of Tubulin-binding Proteins Provides Insight on Subcellular Trafficking, Metabolic Channeling, and Signaling in Plant Cells
Bibliographic record
Abstract
Microtubules play an essential role in the growth and development of plants and are known to be involved in regulating many cellular processes ranging from translation to signaling. In this article, we describe the proteomic characterization of Arabidopsis tubulin-binding proteins that were purified using tubulin affinity chromatography. Microtubule co-sedimentation assays indicated that most, if not all, of the proteins in the tubulin-binding protein fraction possessed microtubule-binding activity. Two-dimensional gel electrophoresis of the tubulin-binding protein fraction was performed, and 86 protein spots were excised and analyzed for protein identification. A total of 122 proteins were identified with high confidence using LC-MS/MS. These proteins were grouped into six categories based on their predicted functions: microtubule-associated proteins, translation factors, RNA-binding proteins, signaling proteins, metabolic enzymes, and proteins with other functions. Almost one-half of the proteins identified in this fraction were related to proteins that have previously been reported to interact with microtubules. This study represents the first large-scale proteomic identification of eukaryotic cytoskeleton-binding proteins, and provides insight on subcellular trafficking, metabolic channeling, and signaling in plant 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".