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.

Resolving a guanine-quadruplex structure in the SARS-CoV-2 genome through circular dichroism and multiscale molecular modeling.

The genome of SARS-CoV-2 coronavirus is made up of a single-stranded RNA fragment that can assume a specific secondary structure, whose stability can influence the virus's ability to reproduce. Recent studies have identified putative guanine quadruplex sequences in SARS-CoV-2 genome fragments that are involved in coding for both structural and non-structural proteins. In this contribution, we focus on a specific G-rich sequence referred to as RG-2, which codes for the non-structural protein 10 (Nsp10) and assumes a guanine-quadruplex (G4) arrangement. We provide the secondary structure of RG-2 G4 at atomistic resolution by molecular modeling and simulation, validated by the superposition of experimental and calculated electronic circular dichroism spectra. Through both experimental and simulation approaches, we have demonstrated that pyridostatin (PDS), a widely recognized G4 binder, can bind to and stabilize RG-2 G4 more strongly than RG-1, another G4 forming sequence that was previously proposed as a potential target for antiviral drug candidates. Overall, this study highlights RG-2 as a valuable target to inhibit the translation and replication of SARS-CoV-2, paving the way towards original therapeutic approaches against emerging RNA viruses.

Virtual Screening Strategy and In Vitro Tests to Identify New Inhibitors of the Immunoproteasome.

Immunoproteasome inhibition is a promising strategy for the treatment of hematological malignancies, autoimmune diseases, and inflammatory diseases. The design of non-covalent inhibitors of the immunoproteasome ß1i/ß5i catalytic subunits could be a novel approach to avoid the drawbacks of the known covalent inhibitors, such as toxicity due to off-target binding. In this work, we report the biological evaluation of thirty-four compounds selected from a commercially available collection. These hit compounds are the outcomes of a virtual screening strategy including a dynamic pharmacophore modeling approach onto the ß1i subunit and a pharmacophore/docking approach onto the ß5i subunit. The computational studies were first followed by in vitro enzymatic assays at 100 µM. Only compounds capable of inhibiting the enzymatic activity by more than 50% were characterized in detail using Tian continuous assays, determining the dissociation constant (Ki) of the non-covalent complex where Ki is also the measure of the binding affinity. Seven out of thirty-four hits showed to inhibit ß1i and/or ß5i subunit. Compound 3 is the most active on the ß1i subunit with Ki = 11.84 ± 1.63 µM, and compound 17 showed Ki = 12.50 ± 0.77 µM on the ß5i subunit. Compound 2 showed inhibitory activity on both subunits (Ki = 12.53 ± 0.18 and Ki = 31.95 ± 0.81 on the ß1i subunit and ß5i subunit, respectively). The induced fit docking analysis revealed interactions with Thr1 and Phe31 of ß1i subunit and that represent new key residues as reported in our previous work. Onto ß5i subunit, it interacts with the key residues Thr1, Thr21, and Tyr169. This last hit compound identified represents an interesting starting point for further optimization of ß1i/ß5i dual inhibitors of the immunoproteasome.

APDB: a database on air pollutant characterization and similarity prediction.

The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood-brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it.

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.

EMBER-Embedding Multiple Molecular Fingerprints for Virtual Screening.

In recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different "spectra" to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands' bioactivity on a data set containing twenty protein kinases with similar binding sites to CDK1. The data set itself is presented, and the architecture is explained in detail along with its training procedure. We report experimental results and an explainability analysis to assess the contribution of each fingerprint to different targets.

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.

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.

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.

SF3B1 modulators affect key genes in metastasis and drug influx: a new approach to fight pancreatic cancer chemoresistance.

Aim: Because mutations of splicing factor 3B subunit-1 (SF3B1) have been identified in 4% of pancreatic ductal adenocarcinoma (PDAC) patients, we investigated the activity of new potential inhibitors of SF3B1 in combination with gemcitabine, one of the standard drugs, in PDAC cell lines. Methods: One imidazo[2,1-b][1,3,4]thiadiazole derivative (IS1) and three indole derivatives (IS2, IS3 and IS4), selected by virtual screening from an in-house library, were evaluated by the sulforhodamine-B and wound healing assay for their cytotoxic and antimigratory activity in the PDAC cells SUIT-2, Hs766t and Panc05.04, the latter harbouring the SF3B1 mutations. The effects on the splicing pattern of proto-oncogene recepteur d'origine nantais (RON) and the gemcitabine transporter human equilibrative nucleoside transporter-1 (hENT1) were assessed by PCR, while the ability to reduce tumour volume was tested in spheroids of primary PDAC cells. Results: The potential SF3B1 modulators inhibited PDAC cell proliferation and prompted induction of cell death. All compounds showed an interesting anti-migratory ability, associated with splicing RON/ΔRON shift in SUIT-2 cells after 24 h exposure. Moreover, IS1 and IS4 potentiated the sensitivity to gemcitabine in both conventional 2D monolayer and 3D spheroid cultures, and these results might be explained by the statistically significant increase in hENT1 expression (P < 0.05 vs. untreated control cells), potentially reversing PDAC chemoresistance. Conclusion: These results support further studies on new SF3B1 inhibitors and the role of RON/hENT1 modulation to develop effective drug combinations against PDAC.

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.

Convolutional architectures for virtual screening.

BACKGROUND: A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy. RESULTS: A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% in high precision discrimination). CONCLUSION: The proposed architecture outperforms state-of-the-art ML approaches, and some interesting insights on molecular fingerprints are devised.

Targeting SARS-CoV-2 RBD Interface: a Supervised Computational Data-Driven Approach to Identify Potential Modulators.

Coronavirus disease 2019 (COVID-19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARS-CoV-2 Spike (S) glycoprotein to host angiotensin-converting enzyme 2 (ACE2). The interaction is mediated by the receptor-binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARS-CoV-2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the bi-molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.

A New Family of Jumonji C Domain-Containing KDM Inhibitors Inspired by Natural Product Purpurogallin.

Aberrant epigenetic modifications are involved in cancer development. Jumonji C domain-containing histone lysine demethylases (KDMs) are found mainly up-regulated in breast, prostate, and colon cancer. Currently, growing interest is focusing on the identification and development of new inhibitors able to block the activity of KDMs and thus reduce tumor progression. KDM4A is known to play a role in several cellular physiological processes, and was recently found overexpressed in a number of pathological states, including cancer. In this work, starting from the structure of purpurogallin 9aa, previously identified as a natural KDM4A inhibitor, we synthesized two main sets of compound derivatives in order to improve their inhibitory activity against KDM4A in vitro and in cells, as well as their antitumor action. Based on the hypothetical biogenesis of the 5-oxo-5H-benzo[7]annulene skeleton of the natural product purpurogallin (Salfeld, 1960; Horner et al., 1961; Dürckheimer and Paulus, 1985; Tanaka et al., 2002; Yanase et al., 2005) the pyrogallol and catechol units were first combined with structural modifications at different positions of the aryl ring using enzyme-mediated oxidative conditions, generating a series of benzotropolone analogs. Two of the synthetic analogs of purpurogallin, 9ac and 9bc, showed an efficient inhibition (50 and 80%) of KDM4A in enzymatic assays and in cells by increasing levels of its specific targets, H3K9me3/2 and H3K36me3. However, these two compounds/derivatives did not induce cell death. We then synthesized a further set of analogs of these two compounds with greater structural diversification. The most potent of these analogs, 9bf, displayed the highest KDM4A inhibitory enzymatic activity in vitro (IC50 of 10.1 and 24.37 µM) in colon cancer cells, and the strongest antitumor action in several solid and hematological human cancer cell lines with no toxic effect in normal cells. Our findings suggest that further development of this compound and its derivatives may lead to the identification of new therapeutic antitumor agents acting through inhibition of KDM4A.

3-(6-Phenylimidazo [2,1-b][1,3,4]thiadiazol-2-yl)-1H-Indole Derivatives as New Anticancer Agents in the Treatment of Pancreatic Ductal Adenocarcinoma.

A new series of imidazo[2,1-b][1,3,4]thiadiazole derivatives was efficiently synthesized and screened for their in vitro antiproliferative activity on a panel of pancreatic ductal adenocarcinoma (PDAC) cells, including SUIT-2, Capan-1 and Panc-1. Compounds 9c and 9l, showed relevant in vitro antiproliferative activity on all three pre-clinical models with half maximal inhibitory concentration (IC50) ranging from 5.11 to 10.8 µM, while the compounds 9e and 9n were active in at least one cell line. In addition, compound 9c significantly inhibited the migration rate of SUIT-2 and Capan-1 cells in the scratch wound-healing assay. In conclusion, our results will support further studies to increase the library of imidazo [2,1-b][1,3,4] thiadiazole derivatives for deeper understanding of the relationship between biological activity of the compounds and their structures in the development of new antitumor compounds against pancreatic diseases.

In Silico Insights towards the Identification of NLRP3 Druggable Hot Spots.

NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer's disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are currently working on the discovery of selective modulators of NLRP3 inflammasome. Unfortunately, limited structural data are available for this target. To date, MCC950 represents one of the most promising noncovalent NLRP3 inhibitors. Recently, a possible region for the binding of MCC950 to the NLRP3 protein was described but no details were disclosed regarding the key interactions. In this communication, we present an in silico multiple approach as an insight useful for the design of novel NLRP3 inhibitors. In detail, combining different computational techniques, we propose consensus-retrieved protein residues that seem to be essential for the binding process and for the stabilization of the protein-ligand complex.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge.

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

The Use of Dynamic Pharmacophore in Computer-Aided Hit Discovery: A Case Study.

In recent years pharmacophore modeling has become increasingly popular due to the development of software solutions and improvement in algorithms that allowed researchers to focus on interactions between protein and ligands instead of technical details of the software. At the same time, progress in computer hardware made molecular dynamics (MD) simulations on regular PC hardware possible. MD simulations are usually used, within the virtual screening process, to take into account the flexibility of the target and studying it in more realistic way. In order to do so, it is customary to use simulations before the virtual screening process and then use them for collecting some specific conformation of the target used. Furthermore, some researchers have demonstrated that the use of multiple crystal structures of the same protein can be valuable to better explore the role of the ligand within the binding pocket and then evaluate the most important interactions that are created during the host-guest recognition process. Findings derived from the MD analysis, especially focused on interactions, can be in fact exploited as features for pharmacophore generation or constraints to be used in the molecular docking as integrated steps of the whole virtual screening process. In this chapter, we will present the recent advances in the field pharmacophore modeling combined with the use of MD, a field well explored by our research group in the last 2 years.