Evidence map›Paper›PMID 39651287›Full record

ArticlebioRxiv : the preprint server for biology2024

Fluorescence based live cell imaging identifies exon 14 skipped hepatocyte growth factor receptor (MET) degraders.

Jayapal Reddy Mallareddy, Lin Yang, Wan-Hsin Lin, Ryan Feathers, Jennifer Ayers-Ringler, Ezequiel Tolosa, Amritha G Kizhake, Smitha Kizhake, Sydney P Kubica, Lidia Boghean and 12 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors.

Jayapal Reddy Mallareddy
Lin Yang
Wan-Hsin Lin
Ryan Feathers
Jennifer Ayers-Ringler
Ezequiel Tolosa
Amritha G Kizhake
Smitha Kizhake
Sydney P Kubica
Lidia Boghean
Sophie Alvarez
Michael J Naldrett
Sarbjit Singh
Sandeep Rana
Muhammad Zahid
Janet Schaefer-Klein
Anja Roden
Farhad Kosari
Panos Z Anastasiadis
Mitesh Borad
Amarnath Natarajan
Aaron S MansfieldORCID 0000-0002-9483-6903

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite ongoing efforts to employ structure-based methods to discover targeted protein degraders (TPD), the prevailing strategy continues to be the synthesis of a focused set of heterobifunctional compounds and screen them for target protein degradation. Here we used a fluorescence based live cell imaging screen to identify degraders that target exon 14 skipped hepatocyte growth factor receptor (MET). MET is a known oncogenic driver. MET exon 14 skipping mutations (METex14Δ) are found in lung cancers and result in the loss of a degron that is required for E3-ligase recognition and subsequent ubiquitination, prolonging the half-life and oncogenicity of MET. Since proteolysis targeting chimeras (PROTACs) are heterobifunctional molecules that promote target degradation by the proteosome, we sought to restore degradation of MET lost with METex14Δ using a MET-targeting PROTAC. We generated a library of sixty PROTACs of which 37 used the MET inhibitor capmatinib as the protein of interest targeting ligand. We screened this PROTAC library for targeted degradation of METex14Δ-GFP using live cell imaging. We benchmarked out MET-targeting PROTACs to that of a previously reported MET-targeting PROTAC, SJF8240. Curve fitting live cell imaging data affords determination of time required to degrade 50% of the target protein (DT50), which was used in determining structure activity relationships. A promising candidate, 48-284, identified from the screen, exhibited classic PROTAC characteristics, was > 15-fold more potent than SJF8240, had fewer off targets compared to SJF8240, and degraded MET in multiple cell lines.

Identifiers

PMID39651287
PMCPMC11623520

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.