Ligand Recognition Specificity of Leukocyte Integrin α<sub>M</sub>β<sub>2</sub> (Mac-1, CD11b/CD18) and Its Functional Consequences
Bibliographic record
Abstract
The broad recognition specificity exhibited by integrin α(M)β2 (Mac-1, CD11b/CD18) has allowed this adhesion receptor to play innumerable roles in leukocyte biology, yet we know little about how and why α(M)β2 binds its multiple ligands. Within α(M)β2, the α(M)I-domain is responsible for integrin's multiligand binding properties. To identify its recognition motif, we screened peptide libraries spanning sequences of many known protein ligands for α(M)I-domain binding and also selected the α(M)I-domain recognition sequences by phage display. Analyses of >1400 binding and nonbinding peptides derived from peptide libraries showed that a key feature of the α(M)I-domain recognition motif is a small core consisting of basic amino acids flanked by hydrophobic residues. Furthermore, the peptides selected by phage display conformed to a similar pattern. Identification of the recognition motif allowed the construction of an algorithm that reliably predicts the α(M)I-domain binding sites in the α(M)β2 ligands. The recognition specificity of the α(M)I-domain resembles that of some chaperones, which allows it to bind segments exposed in unfolded proteins. The disclosure of the α(M)β2 binding preferences allowed the prediction that cationic host defense peptides, which are strikingly enriched in the α(M)I-domain recognition motifs, represent a new class of α(M)β2 ligands. This prediction has been tested by examining the interaction of α(M)β2 with the human cathelicidin peptide LL-37. LL-37 induced a potent α(M)β2-dependent cell migratory response and caused activation of α(M)β2 on neutrophils. The newly revealed recognition specificity of α(M)β2 toward unfolded protein segments and cationic proteins and peptides suggests that α(M)β2 may serve as a previously proposed "alarmin" receptor with important roles in innate host defense.
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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".