Proteomic Approaches for the Study of Electrical Synapses and Associated Protein-Interaction Complexes
Bibliographic record
Abstract
Recent advances in the identification and analysis of protein–protein interaction complexes associated with synapses and synaptic proteins deepened not only our insights into the molecular composition and dynamic structural makeup of interneuronal connections but contributed also significantly to our understanding of the molecular and mechanistic aspects underlying functional plasticity in neuronal networks. In particular proteome analytical tools, combining traditional isolation protocols with modern mass spectrometric approaches, were utilized successfully for the molecular analysis of chemical synapses and other neuronal subcellular structures revealing new and exciting insights into the temporal and spatial changes of the proteins composing or associated with for example synaptic vesicles, synaptic membranes, or postsynaptic densities (PSDs). Proteomic approaches may thus offer also a chance to gain valuable insights into the so far elusive molecular composition of electrical synapses, the Cinderella fated and long neglected little brethren of “classical” chemical synapses. In this chapter we provide an experimental basis of how such an analysis can be designed, with a major focus on the most abundant electrical synapse protein, connexin36.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".