The transcription factor NF-E2-related factor 2 (Nrf2) is a master regulator of a genetic program, termed the phase 2 response, that controls redox homeostasis and participates in multiple aspects of physiology and pathology. Nrf2 protein stability is regulated by two E3 ubiquitin ligase adaptors, Keap1 and β-TrCP, the latter of which was only recently reported. Here, two-dimensional (2D) gel electrophoresis and site-directed mutagenesis allowed us to identify two serines of Nrf2 that are phosphorylated by glycogen synthase kinase 3β (GSK-3β) in the sequence DSGISL. Nuclear magnetic resonance studies defined key residues of this phosphosequence involved in docking to the WD40 propeller of β-TrCP, through electrostatic and hydrophobic interactions. We also identified three arginine residues of β-TrCP that participate in Nrf2 docking. Intraperitoneal injection of the GSK-3 inhibitor SB216763 led to increased Nrf2 and heme oxygenase-1 levels in liver and hippocampus. Moreover, mice with hippocampal absence of GSK-3β exhibited increased levels of Nrf2 and phase 2 gene products, reduced glutathione, and decreased levels of carbonylated proteins and malondialdehyde. This study establishes the structural parameters of the interaction of Nrf2 with the GSK-3/β-TrCP axis and its functional relevance in the regulation of Nrf2 by the signaling pathways that impinge on GSK-3.

Motivation: Scoring functions provided by the docking software are still a major limiting factor in virtual screening (VS) process to classify compounds. Score analysis of the docking is not able to find out all active compounds. This is due to a bad estimation of the ligand binding energies. Making the assumption that active compounds should have specific contacts with their target to display activity, it would be possible to discriminate active compounds from inactive ones with careful analysis of interatomic contacts between the molecule and the target. However, compounds clustering is very tedious due to the large number of contacts extracted from the different conformations proposed by docking experiments. Results: Structural analysis of docked structures is processed in three steps: (i) a Kohonen self-organizing map (SOM) training phase using drug–protein contact descriptors followed by (ii) an unsupervised cluster analysis and (iii) a Newick file generation for results visualization as a tree. The docking poses are then analysed and classified quickly and automatically by AuPosSOM (Automatic analysis of Poses using SOM). AuPosSOM can be integrated into strategies for VS currently employed. We demonstrate that it is possible to discriminate active compounds from inactive ones using only mean protein contacts' footprints calculation from the multiple conformations given by the docking software. Chemical structure of the compound and key binding residues information are not necessary to find out active molecules. Thus, contact–activity relationship can be employed as a new VS process. Availability: AuPosSOM is available at http://www.aupossom.com. Contact:contact@aupossom.com; gildas.bertho@parisdescartes.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Contact-based ligand-clustering approach for the identification of active compounds in virtual screening Alexey B Mantsyzov,1 Guillaume Bouvier,2 Nathalie Evrard-Todeschi,1 Gildas Bertho11Université Paris Descartes, Sorbonne, Paris, France; 2Institut Pasteur, Paris, France Evaluation of docking results is one of the most important problems for virtual screening and in silico drug design. Modern approaches for the identification of active compounds in a large data set of docked molecules use energy scoring functions. One of the general and most significant limitations of these methods relates to inaccurate binding energy estimation, which results in false scoring of docked compounds. Automatic analysis of poses using self-organizing maps (AuPosSOM) represents an alternative approach for the evaluation of docking results based on the clustering of compounds by the similarity of their contacts with the receptor. A scoring function was developed for the identification of the active compounds in the AuPosSOM clustered dataset. In addition, the AuPosSOM efficiency for the clustering of compounds and the identification of key contacts considered as important for its activity, were also improved. Benchmark tests for several targets revealed that together with the developed scoring function, AuPosSOM represents a good alternative to the energy-based scoring functions for the evaluation of docking results.Keywords: scoring, docking, virtual screening, CAR, AuPosSOM