Molecular Epitope Determination of Aptamer Complexes of the Multidomain Protein C‐Met by Proteolytic Affinity‐Mass Spectrometry
Bibliographic record
Abstract
Abstract C‐Met protein is a glycosylated receptor tyrosine kinase of the hepatocyte growth factor (HGF), composed of an α and a β chain. Upon ligand binding, C‐Met transmits intracellular signals by a unique multi‐substrate docking site. C‐Met can be aberrantly activated leading to tumorigenesis and other diseases, and has been recognized as a biomarker in cancer diagnosis. C‐Met aptamers have been recently considered a useful tool for detection of cancer biomarkers. Herein we report a molecular interaction study of human C‐Met expressed in kidney cells with two DNA aptamers of 60 and 64 bases (CLN0003 and CLN0004), obtained using the SELEX ( S ystematic E volution of L igands by Ex ponential Enrichment) procedure. Epitope peptides of aptamer‐C‐Met complexes were identified by proteolytic affinity‐mass spectrometry in combination with SPR biosensor analysis (PROTEX‐SPR‐MS), using high‐pressure proteolysis for efficient digestion. High affinities ( K D , 80–510 nM) were determined for aptamer‐C‐Met complexes, with two‐step binding suggested by kinetic analysis. A linear epitope, C‐Met (381–393) was identified for CLN0004, while the CLN0003 aptamer revealed an assembled epitope comprised of two peptide sequences, C‐Met (524–543) and C‐Met (557–568). Structure modeling of C‐Met‐aptamers were consistent with the identified epitopes. Specificities and affinities were ascertained by SPR analysis of the synthetic epitope peptides. The high affinities of aptamers to C‐Met, and the specific epitopes revealed render them of high interest for cellular diagnostic studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".