Pattern associated modelling for discovery of novel protein motifs in the macrophage scavenger receptors (IRM5P.703)
Bibliographic record
Abstract
Abstract Class A scavenger receptors, including scavenger receptor A and macrophage receptor with collagenous structure, are surface proteins that bind modified endogenous ligands and bacterial components. As these receptors play a key role in innate immunity, they are associated with a range of infectious diseases such as pneumonia. Thus, understanding the physical properties of these receptors will provide insight for the development of novel therapeutics. Macrophage receptor with collagenous structure is amongst the least well-characterized members of this family and is expressed in macrophages to mediate pathogen recognition. Our investigation addresses the development of an efficient and highly accurate bioinformatics technique through combinatorial usage of Aligned Pattern Clustering, and Multiple Sequence Alignment Pattern Retrieval Program for novel motif discovery in the macrophage scavenger receptor. By utilizing scavenger receptor A and its validated motifs as a model, we successfully validated the feasibility of this bioinformatics technique with a minimum sensitivity of 54% and positive predictive value of 60%. As a subsequent validation of the newly discovered motifs, the top sub-sequences of macrophage receptor with collagenous structure, selected by both programs, were analyzed for their biological functionality. Here, we propose that RGRAE, VFCRMLG, EDAGVE and WGTICDD motifs in the scavenger receptor cysteine-rich domain play an important role in pathogen recognition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".