Development of Fluorescent Substrates and Assays for the Key Autophagy-Related Cysteine Protease Enzyme, ATG4B
Bibliographic record
Abstract
The cysteine protease ATG4B plays a role in key steps of the autophagy process and is of interest as a potential therapeutic target. At an early step, ATG4B cleaves proLC3 isoforms to form LC3-I for subsequent lipidation to form LC3-II and autophagosome membrane insertion. ATG4B also cleaves phosphatidylethanolamine (PE) from LC3-II to regenerate LC3-I, enabling its recycling for further membrane biogenesis. Here, we report several novel assays for monitoring the enzymatic activity of ATG4B. An assay based on mass spectrometric analysis and quantification of cleavage of the substrate protein LC3-B was developed and, while useful for mechanistic studies, was not suitable for high throughput screening (HTS). A doubly fluorescent fluorescence resonance energy transfer (FRET) ligand YFP-LC3B-EmGFP (FRET-LC3) was constructed and shown to be an excellent substrate for ATG4B with rates of cleavage similar to that for LC3B itself. A HTS assay to identify candidate inhibitors of ATG4B utilizing FRET-LC3 as a substrate was developed and validated with a satisfactory Z' factor and high signal-to-noise ratio suitable for screening small molecule libraries. Pilot screens of the 1,280-member library of pharmacologically active compounds (LOPAC(™)) and a 3,481-member library of known drugs (KD2) gave hit rates of 0.6% and 0.5% respectively, and subsequent titrations confirmed ATG4B inhibitory activity for three compounds, both in the FRET and mass spectrometry assays. The FRET- and mass spectrometry-based assays we have developed will allow for both HTS for inhibitors of ATG4B and mechanistic approaches to study inhibition of a major component of the autophagy pathway.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".