Exploring the Collagen-binding Site of the DDR1 Tyrosine Kinase Receptor
Bibliographic record
Abstract
Discoidin domain receptors 1 and 2 (DDR1 and DDR2) are tyrosine kinase receptors activated by triple-helical collagens. Aberrant expression and signaling of these receptors have been implicated in several human diseases linked to accelerated matrix degradation and remodeling including tumor invasion, atherosclerosis and liver fibrosis. The objective of this study is to characterize the collagen-binding sites in the discoidin domains of DDR1 and DDR2 at a molecular level. We expressed glutathione S-transferase fusion proteins containing the discoidin and extracellular domains of DDR1 and DDR2 in insect cells and subjected them to a solid-phase collagen-binding assay. We found high affinity binding of the DDR extracellular domains to immobilized type I collagen and confirmed the discoidin-collagen interaction with an enzyme-linked immunosorbent assay-based read-out. Furthermore, we created a three-dimensional model of the DDR1 discoidin domain based on the related domains of blood coagulation factors V and VIII. This model predicts the presence of four neighboring, surface-exposed loops that are topologically equivalent to a major phospholipid-binding site in factors V and VIII. To test the involvement of these loops in collagen binding, we mutated individual amino acid residues to alanine or deleted short sequence stretches within these loops. We found that several residues within loop 1 (Ser-52-Thr-57) and loop 3 (Arg-105-Lys-112) as well as Ser-175 in loop 4 are critically involved in collagen binding. Our structure-function analysis of the DDR discoidin domains provides new insights into this non-integrin-mediated collagen-signaling mechanism and may ultimately lead to the design of small molecule inhibitors that interfere with aberrant DDR function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".