Trapping an enzyme to map its network of influence within the cell
AI-generated hypothesis · Pre-publication · To be tested experimentally
Table of contents — full brief
- Hypothesis and mechanismCausal chain, key assumptions, residual unknowns
- State of the artVerified references and counter-evidence (DOIs)
- Falsifiable predictionsQuantitative bounds, statistical tests, H0
- Experimental protocolThree phases — in silico → minimal → full
- Impact analysisNovelty, residual gaps, available data
- Panel reviewFive personas + meta-review
Verified references
5 of 10 references- DOI: 10.1021/jacs.0c04527 ↗
Discovery of a Potent and Selective Covalent Inhibitor and Activity-Based Probe for the Deubiquitylating Enzyme UCHL1, with Antifibrotic Activity
2019 - DOI: 10.1158/1078-0432.CCR-22-1215 ↗
A cathepsin targeted quenched activity-based probe facilitates enhanced detection of human tumors during resection
2022 - DOI: 10.1021/acs.jproteome.6b00938 ↗
Quantitative metaproteomics and activity-based probe enrichment reveals significant alterations in protein expression from a mouse model of inflammatory bowel disease
2017 - DOI: 10.1038/s41589-023-01392-5 ↗
Activity-based profiling of cullin–RING E3 networks by conformation-specific probes
2023 - DOI: 10.1073/pnas.1423344112 ↗
Empirical inference of circuitry and plasticity in a kinase signaling network
2015
+ 5 more references
Detailed panel scores
The protocol incorporates an in silico validation phase (Phase 1) with explicit GO/NO-GO criteria, which permits the filtering of targets and conditions before commitment to costly experiments — a best practice in sequential experimental design.
The hypothesis convincingly exploits the mechanistic specificity of ABPs (activity-dependent, irreversible inhibition) to create a temporally precise perturbation, which is a clear advantage over reversible inhibitors or genetic perturbations that have longer and less controllable delays in action.
The idea of using an ABP probe as a temporally precise causal intervention is conceptually elegant and addresses a genuine methodological need in network inference.
Captive and solvent market: large pharmaceutical companies (Novartis, Pfizer, Roche) and biotechs in oncology/neurodegeneration (e.g., Alnylam for DUBs) would pay to validate orphan enzymatic targets or to deconvolute mechanisms of resistance to inhibitors, with a typical R&D budget of €500k–2M per target validation project.
A mechanistically elegant and disruptive hypothesis is proposed: the use of ABPs as causal perturbagens for network inference, moving beyond simple imaging or reversible inhibition.
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