Recognition Specificity of Leukocyte Integrin α <b> <sub>M</sub> β </b> <b> <sub>2</sub> (Mac‐1, CD11b/CD18) and its Functional Consequences </b>
Bibliographic record
Abstract
The broad recognition specificity exhibited by integrin αMβ2 has allowed this adhesion receptor to play innumerable roles in leukocyte biology, yet we know little how and why αMβ2 binds its ligands. Within αMβ2, the αMI-domain is responsible for integrin's multiligand binding properties. To determine its recognition motif, we screened peptide libraries spanning sequences of many known protein ligands for the αMI-domain binding and also selected the αMI-domain recognition sequences by phage display. A key feature of the αMI-domain recognition motif is a small core consisting of basic amino acids flanked by hydrophobic residues. Identification of the motif allowed the construction of an algorithm which reliably predicts the αMI-domain binding sites in the αMβ2 ligands. The recognition specificity of the αMI-domain resembles that of some chaperones which enables it to bind segments exposed by protein denaturation. The disclosure of the αMβ2 binding preferences allowed the prediction that cationic host defense peptides, which are strikingly enriched in the aMI-domain recognition motifs, represent a new class of αMβ2ligands. This prediction has been tested by examining the interaction of αMβ2 with the human cathelicidin peptide LL-37. LL-37 induced a potent αMβ2-dependent cell migratory response, caused activation of αMβ2 on neutrophils and αMβ2-mediated signaling in monocytes. The newly revealed recognition specificity of αMβ2 towards unfolded protein segments and cationic proteins/peptides suggests that αMβ2 may serve as a previously proposed “alarmin” receptor with important roles in innate host defense. NIH HL 63199
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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 | 0.000 |
| 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".