Activity-based Protein Profiling Approaches for Transplantation
Bibliographic record
Abstract
Enzyme activity may be more pathophysiologically relevant than enzyme quantity and is regulated by changes in conformational status that are undetectable by traditional proteomic approaches. Further, enzyme activity may provide insights into rapid physiological responses to inflammation/injury that are not dependent on de novo protein transcription. Activity-based protein profiling (ABPP) is a chemical proteomic approach designed to characterize and identify active enzymes within complex biological samples. Activity probes have been developed to interrogate multiple enzyme families with broad applicability, including but not limited to serine hydrolases, cysteine proteases, matrix metalloproteases, nitrilases, caspases, and histone deacetylases. The goal of this overview is to describe the overall rationale, approach, methods, challenges, and potential applications of ABPP to transplantation research. To do so, we present a case example of urine serine hydrolase ABPP in kidney transplant rejection to illustrate the utility and workflow of this analytical approach. Ultimately, developing novel transplant therapeutics is critically dependent on understanding the pathophysiological processes that result in loss of transplant function. ABPP offers a new dimension for characterizing dynamic changes in clinical samples. The capacity to identify and measure relevant enzyme activities provides fresh opportunities for understanding these processes and may help identify markers of disease activity for the development of novel diagnostics and real-time monitoring of patients. Finally, these insights into enzyme activity may also help to identify new transplant therapeutics, such as enzyme-specific inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".