In-silico guided chemical exploration of KDM4A fragments hits.

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.

KUALA: a machine learning-driven framework for kinase inhibitors repositioning.

The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases .

Galectin-9 and Interferon-Gamma Are Released by Natural Killer Cells upon Activation with Interferon-Alpha and Orchestrate the Suppression of Hepatitis C Virus Infection.

Natural killer (NK) cells mount an immune response against hepatitis C virus (HCV) infection and can be activated by several cytokines, including interleukin-2 (IL-2), IL-15, and interferon-alpha (IFN-α). By exploiting the Huh7.5 hepatoma cell line infected with the HCV JFH1 genome, we provide novel insights into the antiviral effector functions of human primary NK cells after cytokine stimulation. NK cells activated with IFN-α (IFNα-NKs) had enhanced contact-dependent and -independent responses as compared with NK cells activated with IL-2/IL-15 (IL2/IL15-NKs) and could inhibit HCV replication both in vitro and in vivo. Importantly, IFN-α, but not IL-2/IL-15, protected NK cells from the functional inhibition exerted by HCV. By performing flow cytometry, multiplex cytokine profiling, and mass-spectrometry-based proteomics, we discovered that IFNα-NKs secreted high levels of galectin-9 and interferon-gamma (IFN-γ), and by conducting neutralization assays, we confirmed the major role of these molecules in HCV suppression. We speculated that galectin-9 might act extracellularly to inhibit HCV binding to host cells and downstream infection. In silico approaches predicted the binding of HCV envelope protein E2 to galectin-9 carbohydrate-recognition domains, and co-immunoprecipitation assays confirmed physical interaction. IFN-γ, on the other hand, triggered the intracellular expressions of two antiviral gate-keepers in target cells, namely, myxovirus-1 (MX1) and interferon-induced protein with tetratricopeptide repeats 1 (IFIT1). Collectively, our data add more complexity to the antiviral innate response mediated by NK cells and highlight galectin-9 as a key molecule that might be exploited to neutralize productive viral infection.

A Rational Design of α-Helix-Shaped Peptides Employing the Hydrogen-Bond Surrogate Approach: A Modulation Strategy for Ras-RasGRF1 Interaction in Neuropsychiatric Disorders.

In the last two decades, abnormal Ras (rat sarcoma protein)-ERK (extracellular signal-regulated kinase) signalling in the brain has been involved in a variety of neuropsychiatric disorders, including drug addiction, certain forms of intellectual disability, and autism spectrum disorder. Modulation of membrane-receptor-mediated Ras activation has been proposed as a potential target mechanism to attenuate ERK signalling in the brain. Previously, we showed that a cell penetrating peptide, RB3, was able to inhibit downstream signalling by preventing RasGRF1 (Ras guanine nucleotide-releasing factor 1), a neuronal specific GDP/GTP exchange factor, to bind Ras proteins, both in brain slices and in vivo, with an IC50 value in the micromolar range. The aim of this work was to mutate and improve this peptide through computer-aided techniques to increase its inhibitory activity against RasGRF1. The designed peptides were built based on the RB3 peptide structure corresponding to the α-helix of RasGRF1 responsible for Ras binding. For this purpose, the hydrogen-bond surrogate (HBS) approach was exploited to maintain the helical conformation of the designed peptides. Finally, residue scanning, MD simulations, and MM-GBSA calculations were used to identify 18 most promising α-helix-shaped peptides that will be assayed to check their potential activity against Ras-RasGRF1 and prevent downstream molecular events implicated in brain disorders.

A Computer-Based Methodology to Design Non-Standard Peptides Potentially Able to Prevent HOX-PBX1-Associated Cancer Diseases.

In the last decades, HOX proteins have been extensively studied due to their pivotal role in transcriptional events. HOX proteins execute their activity by exploiting a cooperative binding to PBX proteins and DNA. Therefore, an increase or decrease in HOX activity has been associated with both solid and haematological cancer diseases. Thus, inhibiting HOX-PBX interaction represents a potential strategy to prevent these malignancies, as demonstrated by the patented peptide HTL001 that is being studied in clinical trials. In this work, a computational study is described to identify novel potential peptides designed by employing a database of non-natural amino acids. For this purpose, residue scanning of the HOX minimal active sequence was performed to select the mutations to be further processed. According to these results, the peptides were point-mutated and used for Molecular Dynamics (MD) simulations in complex with PBX1 protein and DNA to evaluate complex binding stability. MM-GBSA calculations of the resulting MD trajectories were exploited to guide the selection of the most promising mutations that were exploited to generate twelve combinatorial peptides. Finally, the latter peptides in complex with PBX1 protein and DNA were exploited to run MD simulations and the ΔGbinding average values of the complexes were calculated. Thus, the analysis of the results highlighted eleven combinatorial peptides that will be considered for further assays.

Exploring Molecular Contacts of MUC1 at CIN85 Binding Interface to Address Future Drug Design Efforts.

The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.

Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket.

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.