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.

Effect of bleaching treatments on the adhesion of orthodontic brackets: a systematic review.

BACKGROUND: Nowadays bleaching procedures have gained popularity in orthodontic patients. Peroxide and Carbamide acids are the common agents which are used in in-office and at home bleaching techniques. Consequently, the Bonding adhesion to the enamel can be influenced by the orthodontic phase and the residual peroxide might interfere with the polymerization and the adhesion of the brackets. Frequent debonding of the brackets from teeth after the bleaching procedure could cause the lengthening of the therapy and promote irregularities on enamel surface derived from an additional bonding phase of the brackets. The aim of this systematic review is to appraise the influence regarding the effect of the bleaching procedure on the bond strength of orthodontic brackets. METHODS: An electronic database search was performed. Search terms included: bleaching, brackets, adhesion; data were extracted and summarized. Risk of bias was assessed using the Chocrane risk of bias tool, adapted for in vitro studies. RESULTS: A total of 8689 articles were screened and 11 studies met the inclusion criteria of this systematic review. 1000 teeth of human and bovine origin were analyzed for the shear bond strength (SBS) of stainless and ceramic brackets after the bleaching treatments. All the authors divided the groups in different subgroups with different bleaching agents and in different concentration. The SBS value allowed to demonstrate the necessity to delay the bonding of the brackets for two weeks after a bleaching treatment and its improvement when tooth mousse or antioxidants agents are used. CONCLUSIONS: The SBS values and the delay of the bonding procedure must be considered in dental practice and clinical strategies are necessary in order to avoid drawbacks which could cause the debonding of the brackets after bleaching due to the alterations of the dental substrate, thus interfering with the orthodontic treatments.

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 .

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 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.

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.

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.

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.

The possible consequences for cognitive functions of external electric fields at power line frequency on hippocampal CA1 pyramidal neurons.

The possible effects on cognitive processes of external electric fields, such as those generated by power line pillars and household appliances are of increasing public concern. They are difficult to study experimentally, and the relatively scarce and contradictory evidence make it difficult to clearly assess these effects. In this study, we investigate how, why and to what extent external perturbations of the intrinsic neuronal activity, such as those that can be caused by generation, transmission and use of electrical energy can affect neuronal activity during cognitive processes. For this purpose, we used a morphologically and biophysically realistic three-dimensional model of CA1 pyramidal neurons. The simulation findings suggest that an electric field oscillating at power lines frequency, and environmentally measured strength, can significantly alter both the average firing rate and temporal spike distribution properties of a hippocampal CA1 pyramidal neuron. This effect strongly depends on the specific and instantaneous relative spatial location of the neuron with respect to the field, and on the synaptic input properties. The model makes experimentally testable predictions on the possible functional consequences for normal hippocampal functions such as object recognition and spatial navigation. The results suggest that, although EF effects on cognitive processes may be difficult to occur in everyday life, their functional consequences deserve some consideration, especially when they constitute a systematic presence in living environments.

Effect of the initial synaptic state on the probability to induce long-term potentiation and depression.

Long-term potentiation (LTP) and long-term depression (LTD) are the two major forms of long-lasting synaptic plasticity in the mammalian neurons, and are directly related to higher brain functions such as learning and memory. Experimentally, they are characterized by a change in the strength of a synaptic connection induced by repetitive and properly patterned stimulation protocols. Although many important details of the molecular events leading to LTP and LTD are known, experimenters often report problems in using standard induction protocols to obtain consistent results, especially for LTD in vivo. We hypothesize that a possible source of confusion in interpreting the results, from any given experiment on synaptic plasticity, can be the intrinsic limitation of the experimental techniques, which cannot take into account the actual state and peak conductance of the synapses before the conditioning protocol. In this article, we investigate the possibility that the same experimental protocol may result in different consequences (e.g., LTD instead of LTP), according to the initial conditions of the stimulated synapses, and can generate confusing results. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we study how, why, and to what extent the phenomena observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation for why LTD induction is experimentally difficult to obtain. They also suggest experimentally testable predictions on the stimulation protocols that may be more effective.