Fragment‐Based Drug Discovery of Novel High‐affinity, Selective, and Anti‐inflammatory Inhibitors of the Keap1‐Nrf2 Protein‐Protein Interaction
Bibliographic record
Abstract
Abstract Activating the cytoprotective response of nuclear factor erythroid 2‐related factor 2 (Nrf2) can reduce oxidative stress and inflammation. A promising strategy is to inhibit the protein‐protein interaction between Kelch‐like ECH‐associated protein 1 (Keap1) and Nrf2 using noncovalent compounds that target the Keap1 Kelch domain. These compounds may be more specific than covalent Keap1‐reacting Nrf2 activators. However, the development of drug‐like noncovalent Keap1‐Nrf2 inhibitors faces challenges due to the size and polarity of the Kelch binding pocket. Here, we present a new series of noncovalent Keap1‐Nrf2 inhibitors developed from a weak fragment hit identified by crystallographic screening. A two‐step growing strategy and optimization guided by several X‐ray cocrystal structures led to compounds with low nanomolar affinities and complete selectivity for Keap1 in a panel of homologous Kelch domains. In cells, compounds 24 and 28 potently activated the expression of Nrf2‐controlled genes and showed anti‐inflammatory effects by downregulating NLRP3 inflammasome and STING signalling activation. RNA sequencing revealed activation of cytoprotective pathways and a different profile from typical covalent Nrf2 activators. This work highlights the potential of fragment‐based drug discovery for challenging targets like Keap1 and introduces novel Keap1‐Nrf2 inhibitors as chemical probes and drug leads.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".