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Background: Lysine demethylase enzymes (KDMs) are an emerging class of therapeutic targets, that catalyse the removal of methyl marks from histone lysine residues regulating chromatin structure and gene expression. KDM4A isoform plays an important role in the epigenetic dysregulation in various cancers and is linked to aggressive disease and poor clinical outcomes. Despite several efforts, the KDM4 family lacks successful specific molecular inhibitors. Results: Herein, starting from a structure-based fragments virtual screening campaign we developed a synergic framework as a guide to rationally design efficient KDM4A inhibitors. Commercial libraries were used to create a fragments collection and perform a virtual screening campaign combining docking and pharmacophore approaches. The most promising compounds were tested in-vitro by a Homogeneous Time-Resolved Fluorescence-based assay developed for identifying selective substrate-competitive inhibitors by means of inhibition of H3K9me3 peptide demethylation. 2-(methylcarbamoyl)isonicotinic acid was identified as a preliminary active fragment, displaying inhibition of KDM4A enzymatic activity. Its chemical exploration was deeply investigated by computational and experimental approaches which allowed a rational fragment growing process. The in-silico studies guided the development of derivatives designed as expansion of the primary fragment hit and provided further knowledge on the structure–activity relationship. Conclusions: Our study describes useful insights into key ligand-KDM4A protein interaction and provides structural features for the development of successful selective KDM4A inhibitors. [ABSTRACT FROM AUTHOR]

Networks-based approaches are often used to analyze gene expression data or protein–protein interactions but are not usually applied to study the relationships between different biomarkers. Given the clinical need for more comprehensive and integrative biomarkers that can help to identify personalized therapies, the integration of biomarkers of different natures is an emerging trend in the literature. Network analysis can be used to analyze the relationships between different features of a disease; nodes can be disease-related phenotypes, gene expression, mutational events, protein quantification, imaging-derived features and more. Since different biomarkers can exert causal effects between them, describing such interrelationships can be used to better understand the underlying mechanisms of complex diseases. Networks as biomarkers are not yet commonly used, despite being proven to lead to interesting results. Here, we discuss in which ways they have been used to provide novel insights into disease susceptibility, disease development and severity. [ABSTRACT FROM AUTHOR]

Background : The BAG6 complex resides in the cytosol and acts as a sorting point to target diverse hydrophobic protein substrates along their appropriate paths, including proteasomal degradation and ER membrane insertion. Composed of a trimeric complex of BAG6, TRC35 and UBL4A, the BAG6 complex is closely associated with SGTA, a co-chaperone from which it can obtain hydrophobic substrates. Methodology and Principal Findings : SGTA consists of an N-terminal dimerisation domain (SGTA_NT), a central tetratricopeptide repeat (TPR) domain, and a glutamine rich region towards the C-terminus. Here we solve a solution structure of the SGTA dimerisation domain and use biophysical techniques to investigate its interaction with two different UBL domains from the BAG6 complex. The SGTA_NT structure is a dimer with a tight hydrophobic interface connecting two sets of four alpha helices. Using a combination of NMR chemical shift perturbation, isothermal titration calorimetry (ITC) and microscale thermophoresis (MST) experiments we have biochemically characterised the interactions of SGTA with components of the BAG6 complex, the ubiquitin-like domain (UBL) containing proteins UBL4A and BAG6. We demonstrate that the UBL domains from UBL4A and BAG6 directly compete for binding to SGTA at the same site. Using a combination of structural and interaction data we have implemented the HADDOCK protein-protein interaction docking tool to generate models of the SGTA-UBL complexes. Significance : This atomic level information contributes to our understanding of the way in which hydrophobic proteins have their fate decided by the collaboration between SGTA and the BAG6 complex. [ABSTRACT FROM AUTHOR]