Characterisation of an allosteric site in PLCγ enzymes and implications for development of their specific inhibitors
Bibliographic record
Abstract
Phospholipase C gamma (PLCγ) enzymes are key components of intracellular signal transduction processes and are involved in disease development, including immune dysregulation, specific cancer types and neurodegeneration. Although recognised as important targets for intervention, validated pharmacological tools are lacking. Here, we demonstrate that inhibitory nucleotides bind directly to an allosteric site at the interface between the PLC-core and regulatory-array unique for PLCγ, underlying their specificity for the PLCγ family. This binding site overlaps with the PLCγ autoinhibitory interface, suggesting that the inhibitory impact of nucleotides involves stabilisation of autoinhibition. We have also analysed disease-linked variants of PLCγ1 and PLCγ2 to show that multiple mechanisms could underpin their gain-of-function phenotype. While the sensitivity of these variants to physiological nucleotide inhibition is reduced, we identified artificial nucleotide compounds that can inhibit such variants not only in vitro but also in cell-based assays. Therefore, our findings suggest a route for development of isozyme specific PLCγ inhibitors allowing further studies of their roles in health 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 | 0.001 |
| 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".