Mapping the MET Receptor Tyrosine Kinase Interactome using Mammalian Membrane Two Hybrid (MaMTH) system
Bibliographic record
Abstract
Background MET receptor tyrosine kinase is a growth factor receptor implicated in majority of cancers including lung, breast, pancreas, ovary and brain. When aberrantly activated, MET exacerbates malignancies by promoting metastasis, anti‐apoptosis and a major hindrance to today’s anti‐cancer therapeutics ‐ cancer drug resistance. Thus, MET is a sought‐out target in cancer drug discovery. Motivation Despite MET’s significance to cancer biology, there lacks a detailed understanding of MET’s biological functions, signalling dynamics and regulatory pathways all of which have resulted in therapies targeting MET‐mediated oncogenicity to be seldom successful. Objective Since protein‐protein interactions (PPIs) govern majority of protein functions within cells, and are, since recently, considered to be druggable, our aim is to map a comprehensive interactome of MET to aid in uncovering novel biology and innovative therapeutic strategies against MET. Methods To map the PPIs of MET, we utilized Mammalian Membrane Two‐Hybrid (MaMTH) assay, a split‐ubiquitin two‐hybrid system, that allows for sensitive PPI mapping of full‐length membrane proteins, at any throughput level, in their native environment. Results We integrated MET into MaMTH pipeline by generating HEK293‐MaMTH reporter cells stably expressing “bait”‐tagged MET under a tetracycline inducible promoter (MET‐MaMTH system). Firstly, we validated the MET‐MaMTH system by several means. We confirmed the inducibility of MET protein with western‐blotting and that the expressed MET localises to plasma membrane using both surface biotinylation assay and imaging. In our pilot PPI screen against previously known interactors of MET, the MET‐MaMTH system detected six out of ten interactors assayed. The system also showed a dose dependent loss of interaction with MET kinase activity dependent interactor, SHC1, upon treatment with MET kinase inhibitor Crizotinib. Secondly, using the validated MET‐MaMTH system, we performed a targeted PPI screen using two MaMTH ORF libraries enriched for their interaction capabilities with MET. The libraries consisted 99 of 121 SH2/PTB domain containing proteins and 57 computationally predicted putative interactors of MET. From the screens, we identified 35 interactors of MET, of which 27 are novel. Extending on these findings, we are currently performing unbiased screening for PPIs of MET in a pool based high‐throughput MaMTH screening platform (MaMTH‐HTS) against 13,000 ORFs of the Human ORFeome v8.1. Conclusion We have characterized a MaMTH PPI mapping platform for MET and using a targeted screening approach, have identified 27 novel interactors of MET. Significance MET’s role in cancers is indisputable and the paucity of a thorough understanding on MET and its associated proteins pose a barrier in the quest towards cancer drug discovery. This study will provide a much‐needed comprehensive PPI map of full‐length MET, not only enhancing our understanding of MET, but also presenting novel PPI targets for desperately needed MET therapeutics. Support or Funding Information Canadian Cancer Society
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".