Prioritization of Therapeutic Targets of Inflammation Using Proteomics, Bioinformatics, and In Silico Cell–Cell Interactomics
Bibliographic record
Abstract
Leukocyte extravasation is a multistep process, involving the movement of leukocytes out of the circulatory system, through vascular endothelium and to the site of tissue damage or infection. Protein-protein interactions play key roles in the extravasation process and have been attractive therapeutic targets for inhibiting inflammation using blocking (or neutralizing) antibodies. These targets include protein-protein interactions between cytokines (or chemokines) and their receptors on leukocytes and between adhesions molecules involving leukocyte-endothelium contacts. A number of therapeutics against these targets are currently used in clinic for treatment of inflammatory disorders, however, they are associated with side-effects partly due to the off-target actions (i.e., nonspecific targets). There is a need for novel targets involved in the leukocyte extravasation process that are specific to leukocyte subsets or to individual inflammatory disorder, and are amenable for drug development (i.e., duggable). In this chapter, we describe a methodology to identify novel "druggable" targets involving protein-protein interactions between activated leukocytes and endothelial cells using a combination of proteomics, bioinformatics and in silico interactomics. The result is a prioritized list of protein-protein interactions in a network consisting of leukocyte-endothelial cell communication and contacts. These prioritized targets can be pursued for the development of therapeutics such as neutralizing antibodies and for their validation through preclinical testing. The method described here provides the workflow to identify and clinically target important cell-cell interactions that are specific/selective for particular inflammatory disorders and to improve currently available therapies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".