Antibody equivalent molecules of the innate immune system: parallels between innate and adaptive immune proteins
Bibliographic record
Abstract
Soluble pattern-recognition innate immune proteins functionally resemble the antibodies of the adaptive immune system. Two major families of such proteins are ficolins and collectins or collagenous lectins (e.g. mannose-binding lectin [MBL], surfactant proteins [SP-A and SP-D] and conglutinin). In general, subunits of ficolins and collectins recognize the carbohydrate arrays of their targets via globular trimeric carbohydrate-recognition domains (CRDs) whereas IgG, IgM and other antibody isotypes recognize proteins via dimeric antigen-binding domains (Fab). Considering the structure and functions of these proteins, ficolins and MBL are analogous to molecules with the complement activating functions of C1q and the target recognition ability of IgG. Although the structure of SP-A is similar to MBL, it does not activate the complement system. Surfactant protein-D and conglutinin could be considered as the collagenous non-complement activating giant IgMs of the innate immune system. Proteins such as peptidoglycan-recognition proteins, pentraxins and agglutinin gp-340/DMBT1 are also pattern-recognition proteins. These proteins may be considered as different isotypes of antibody-like molecules. Proteins such as defensins, cathelicidins and lactoferrins directly or indirectly alter microbes or microbial growth. These proteins may not be considered as antibodies of the innate immune system. Hence, ficolins and collectins could be considered as specialized 'antibodies of the innate immune system' instead of 'ante-antibody' innate immune molecules. The discovery, structure, functions and future research directions of many of these soluble proteins and receptors such as Toll-like and NOD-like receptors are discussed in this special issue of Innate Immunity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".