Electrostatic interactions: From immune receptor assembly to signaling
Bibliographic record
Abstract
Our ability to mount a long-lasting and protective immune response relies on a variety of immune receptors that enable the recognition of ongoing infections, which triggers the adaptation of a myriad of immune cells. The organization of several immune receptors, such as the T cells receptor and several natural killer cell receptors, utilizes different modules for ligand recognition and signaling. These receptors require specific recognition mechanisms between the different modules in order to ensure proper assembly and function. Once assembled, immune receptors must remain inactive in the absence of ligand to prevent the onset of unwanted immune response. Indeed, several mechanisms exist to prevent aberrant immune receptor signaling in the absence of ligand to avert the initiation of uncontrolled autoimmunity. However, once a ligand is recognized, immune receptors must rapidly and specifically engage kinases to initiate highly regulated signaling cascades that lead to the initiation of transcriptional programs that dictate the immune response. Over the last decade, compelling evidence have been presented which suggest that electrostatic interactions are critical for many aspects of immune receptor functions. In the work that follows, we present an overview of the literature that have provided evidence that illustrate how electrostatic interactions regulate immune receptor assembly, inactive state, triggering, and signaling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".