Probing The Structural Requirements of Non-electrophilic Naphthalene-Based Nrf2 Activators.

Activation of the transcription factor Nrf2 has been posited to be a promising therapeutic strategy in a number of inflammatory and oxidative stress diseases due to its regulation of detoxifying enzymes. In this work, we have developed a comprehensive structure-activity relationship around a known, naphthalene-based non-electrophilic activator of Nrf2, and we report highly potent non-electrophilic activators of Nrf2. Computational docking analysis of a subset of the compound series demonstrates the importance of water molecule displacement for affinity, and the X-ray structure of di-amide 12e supports the computational analysis. One of the best compounds, acid 16b, has an IC50 of 61 nM in a fluorescence anisotropy assay and a Kd of 120 nM in a surface plasmon resonance assay. Additionally, we demonstrate that the ethyl ester of 16b is an efficacious inducer of Nrf2 target genes, exhibiting ex vivo efficacy similar to the well-known electrophilic activator, sulforaphane

Probing the structural requirements of non-electrophilic naphthalene-based Nrf2 activators

Activation of the transcription factor Nrf2 has been posited to be a promising therapeutic strategy in a number of inflammatory and oxidative stress diseases due to its regulation of detoxifying enzymes. In this work, we have developed a comprehensive structure-activity relationship around a known, naphthalene-based non-electrophilic activator of Nrf2, and we report highly potent non-electrophilic activators of Nrf2. Computational docking analysis of a subset of the compound series demonstrates the importance of water molecule displacement for affinity, and the X-ray structure of di-amide 12e supports the computational analysis. One of the best compounds, acid 16b, has an IC50 of 61 nM in a fluorescence anisotropy assay and a Kd of 120 nM in a surface plasmon resonance assay. Additionally, we demonstrate that the ethyl ester of 16b is an efficacious inducer of Nrf2 target genes, exhibiting ex vivo efficacy similar to the well-known electrophilic activator, sulforaphane

Combining Structure and Sequence Information Allows Automated Prediction of Substrate Specificities within Enzyme Families

An important aspect of the functional annotation of enzymes is not only the type of reaction catalysed by an enzyme, but also the substrate specificity, which can vary widely within the same family. In many cases, prediction of family membership and even substrate specificity is possible from enzyme sequence alone, using a nearest neighbour classification rule. However, the combination of structural information and sequence information can improve the interpretability and accuracy of predictive models. The method presented here, Active Site Classification (ASC), automatically extracts the residues lining the active site from one representative three-dimensional structure and the corresponding residues from sequences of other members of the family. From a set of representatives with known substrate specificity, a Support Vector Machine (SVM) can then learn a model of substrate specificity. Applied to a sequence of unknown specificity, the SVM can then predict the most likely substrate. The models can also be analysed to reveal the underlying structural reasons determining substrate specificities and thus yield valuable insights into mechanisms of enzyme specificity. We illustrate the high prediction accuracy achieved on two benchmark data sets and the structural insights gained from ASC by a detailed analysis of the family of decarboxylating dehydrogenases. The ASC web service is available at http://asc.informatik.uni-tuebingen.de/