Immigrant-critical alternative media in online conversations.

In this work, we explore the role of immigrant-critical alternative media in shaping collective emotions and users' evaluations of the immigration issue, using a conversational approach and an empirical case of Flashback, a prominent Swedish online platform where many immigration-related discussions take place. Our text and network-based analysis of more than 9,000 conversations during the last election period reveals that the platform users consume and distribute diverging types of media content across a wide ideological spectrum which, however, has a limited influence on the evolution of conversations and users' stances in the immigration debate. Nevertheless, we find that the conversation networks with alternative media content tend to include more negative evaluations of the immigration issue, attracting fewer participants and lasting less than other conversations. We contextualise our findings using Collins' Interaction Ritual Chains (IRC) theory and discuss the conditions under which such online conversations can produce high user involvement and, potentially, participants' radicalisation.

Unspoken Assumptions in Multi-layer Modularity maximization.

A principled approach to recover communities in social networks is to find a clustering of the network nodes into modules (i.e groups of nodes) for which the modularity over the network is maximal. This guarantees partitioning the network nodes into sparsely connected groups of densely connected nodes. A popular extension of modularity has been proposed in the literature so it applies to multi-layer networks, that is, networks that model different types/aspects of interactions among a set of actors. In this extension, a new parameter, the coupling strength ω, has been introduced to couple different copies (i.e nodes) of the same actor with specific weights across different layers. This allows two nodes that refer to the same actor to reward the modularity score with an amount proportional to ω when they appear in the same community. While this extension seems to provide an effective tool to detect communities in multi-layer networks, it is not always clear what kind of communities maximising the generalised modularity can identify in multi-layer networks and whether these communities are inclusive to all possible community structures possible to exist in multi-layer networks. In addition, it has not been thoroughly investigated yet how to interpret ω in real-world scenarios, and whether a proper tuning of ω, if exists, is enough to guarantee an accurate recoverability for different types of multi-layer community structures. In this article, we report the different ways used in the literature to tune ω. We analyse different community structures that can be recovered by maximising the generalised modularity in relation to ω. We propose different models for multi-layer communities in multiplex and time-dependent networks and test if they are recoverable by modularity-maximization community detection methods under any assignment of ω. Our main finding is that only few simple models of multi-layer communities in multiplex and time-dependent networks are recoverable by modularity maximisation methods while more complex models are not accurately recoverable under any assignment of ω.

Quantifying layer similarity in multiplex networks: a systematic study.

Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.

Foundations of Temporal Text Networks.

Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.

Shortest Paths in Multiplex Networks.

The shortest path problem is one of the most fundamental networks optimization problems. Nowadays, individuals interact in extraordinarily numerous ways through their offline and online life (e.g., co-authorship, co-workership, or retweet relation in Twitter). These interactions have two key features. First, they have a heterogeneous nature, and second, they have different strengths that are weighted based on their degree of intimacy, trustworthiness, service exchange or influence among individuals. These networks are known as multiplex networks. To our knowledge, none of the previous shortest path definitions on social interactions have properly reflected these features. In this work, we introduce a new distance measure in multiplex networks based on the concept of Pareto efficiency taking both heterogeneity and weighted nature of relations into account. We then model the problem of finding the whole set of paths as a form of multiple objective decision making and propose an exact algorithm for that. The method is evaluated on five real-world datasets to test the impact of considering weights and multiplexity in the resulting shortest paths. As an application to find the most influential nodes, we redefine the concept of betweenness centrality based on the proposed shortest paths and evaluate it on a real-world dataset from two-layer trade relation among countries between years 2000 and 2015.

Structure-activity relationship and properties optimization of a series of quinazoline-2,4-diones as inhibitors of the canonical Wnt pathway.

Wnt signaling pathway plays a critical role in numerous cellular processes, including tumor initiation, proliferation, invasion/infiltration, metastasis formation and resistance to chemotherapy. In a drug discovery project aimed at the identification of inhibitors of the canonical Wnt pathway, we selected a series of quinazoline 2,4-diones as starting point for the therapeutic treatment of glioblastoma multiforme. Despite of poor physico-chemical properties of hit compound 1, our medicinal chemistry effort allowed the discovery and characterization of lead compound 33 (SEN461), with improved ADME profile, good bioavailability and active in vitro and in vivo in glioblastoma, gastric and sarcoma tumors.

Searching for Dual Inhibitors of the MDM2-p53 and MDMX-p53 Protein-Protein Interaction by a Scaffold-Hopping Approach.

Two libraries of substituted benzimidazoles were designed using a 'scaffold-hopping' approach based on reported MDM2-p53 inhibitors. Substituents were chosen following library enumeration and docking into an MDM2 X-ray structure. Benzimidazole libraries were prepared using an efficient solution-phase approach and screened for inhibition of the MDM2-p53 and MDMX-p53 protein-protein interactions. Key examples showed inhibitory activity against both targets.

Molecular interaction fields and 3D-QSAR studies of p53-MDM2 inhibitors suggest additional features of ligand-target interaction.

The design and optimization of small molecule inhibitors of the murine double minute clone 2-p53 (p53-MDM2) interaction has attracted a great deal of interest as a way to novel anticancer therapies. Herein we report 3D-QSAR studies of 41 small molecule inhibitors based on the use of molecular interaction fields and docking experiments as part of an approach to generating predictive models of MDM2 affinity and shedding further light on the structural elements of the ligand-target interaction. These studies have yielded predictive models explaining much of the variance of the 41 compound training set and satisfactorily predicting with 75% success an external test set of 36 compounds. Not surprisingly, and in full agreement with previous data, inspection of the 3D-QSAR coefficients reveals that the major driving force for potent inhibition is given by the hydrophobic interaction between the inhibitors and the p53 binding cleft of MDM2. More surprisingly, and challenging previous suggestions, the projection of the 3D-QSAR coefficients back onto the experimental structures of MDM2 provides an intriguing hypothesis concerning an active role played by the N-terminal region of MDM2 in ligand binding.

Possible binding site for paclitaxel at microtubule pores.

Taxanes and other microtubule-stabilizing agents comprise an important class of anticancer drugs. It is well known that taxanes act by binding to beta-tubulin on the lumenal side of microtubules. However, experimental evidence obtained in recent years led to the hypothesis of an external site on the microtubule wall to which taxanes and other microtubule-stabilizing agents could bind before being internalized to their lumenal site. In the present study, different computational techniques were combined to explore the possible existence of an exposed and easily accessible binding site for microtubule-stabilizing agents on the outside of microtubules. The results obtained indicate that the conformational rearrangement of the H6-H7 hoop of beta-tubulin can form a suitable pocket on the outer microtubule surface, and that paclitaxel can efficaciously interact with this newly-proposed binding site.

Docking, 3D-QSAR studies and in silico ADME prediction on c-Src tyrosine kinase inhibitors.

Docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis were performed on a wide set of c-Src inhibitors. The study was conducted using a structure-based alignment and by applying the GRID/GOLPE approach. The present 3D-QSAR investigation proved to be of good statistical value, displaying r(2), q(2) and cross-validation SDEP values of 0.94, 0.84 and 0.42, respectively. Moreover, such a model also proved to be capable of predicting the activities of an external test set of compounds. The availability of the 3D structure of the target made possible the interpretation of steric and electrostatic maps within the binding site environment and provided useful insight into the structural requirements for inhibitory activity against c-Src. Two regions whose occupation by hydrophobic portions of ligands would favourably affect the activity were clearly identified. Moreover, hydrogen bond interactions involving residues Met343, Asp406 and Ser347 emerged as playing a key role in determining the affinity of the active inhibitors toward c-Src. Furthermore, the inhibitors bearing a basic nitrogen provided enhanced potency through protonation and salt bridge formation with Asp350. A preliminary pharmacokinetic profile of the molecules under analysis was also drawn on the basis of Volsurf predictions.

Molecular modeling approaches to study the binding mode on tubulin of microtubule destabilizing and stabilizing agents.

Tubulin targeting agents constitute an important class of anticancer drugs. By acting either as microtubule stabilizers or destabilizers, they disrupt microtubule dynamics, thus inducing mitotic arrest and, ultimately, cell death by apoptosis. Three different binding sites, whose exact location on tubulin has been experimentally detected, have been identified so far for antimitotic compound targeting microtubules, namely the taxoid, the colchicine and the vinka alkaloid binding site. A number of ligand- and structure-based molecular modeling studies in this field has been reported over the years, aimed at elucidating the binding modes of both stabilizing and destabilizing agent, as well as the molecular features responsible for their efficacious interaction with tubulin. Such studies are described in this review, focusing on information provided by different modeling approaches on the structural determinants of antitubulin agents and the interactions with the binding pockets on tubulin emerged as fundamental for antitumor activity.To describe molecular modeling approaches applied to date to molecules known to bind microtubules, this paper has been divided into two main parts: microtubule destabilizing (Part 1) and stabilizing (Part 2) agents. The first part includes structure-based and ligand-based approaches to study molecules targeting colchicine (1.1) and vinca alkaloid (1.2) binding sites, respectively. In the second part, the studies performed on microtubule-stabilizing antimitotic agents (MSAA) are described. Starting from the first representative compound of this class, paclitaxel, molecular modeling studies (quantitative structure-activity relationships - QSAR - and structure-based approaches), performed on natural compounds acting with the same mechanism of action and temptative common pharmacophoric hypotheses for all of these compounds, are reported.

Targets looking for drugs: a multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery.

Pharmacophoresthree-dimensional (3D) arrangements of essential features enabling a molecule to exert a particular biological effectconstitute a very useful tool in drug design both in hit discovery and hit-to-lead optimization process. Two basic approaches for pharmacophoric model generation can be used by chemists, depending on the availability or not of the target 3D structure. In view of the rapidly growing number of protein structures that are now available, receptor-based pharmacophore generation methods are becoming more and more used. Since most of them require the knowledge of the 3D structure of the ligand-target complex, they cannot be applied when no compounds targeting the binding site of interest are known. Here, a GRID-based procedure for the generation of receptor-based pharmacophores starting from the knowledge of the sole protein structure is described and successfully applied to address three different tasks in the field of medicinal chemistry.

Synthesis, biological evaluation and SAR study of novel pyrazole analogues as inhibitors of Mycobacterium tuberculosis.

As a continuation of our previous work that turned toward the identification of antimycobacterial compounds with innovative structures, two series of pyrazole derivatives were synthesized by parallel solution-phase synthesis and were assayed as inhibitors of Mycobacterium tuberculosis (MTB), which is the causative agent of tuberculosis. One of these compounds showed high activity against MTB (MIC = 4 microg/mL). The newly synthesized pyrazoles were also computationally investigated to analyze their fit properties to the pharmacophoric model for antitubercular compounds previously built by us and to refine structure-activity relationship analysis.

Structure-based optimization of pyrazolo[3,4-d]pyrimidines as Abl inhibitors and antiproliferative agents toward human leukemia cell lines.

Results from molecular docking calculations and Grid mapping laid the foundations for a structure-based optimization approach to improve the biological properties of pyrazolo-pyrimidine derivatives in terms of inhibition of Abl enzymatic activity and antiproliferative properties toward human leukemia cells. Insertion of halogen substituents with various substitution patterns, suggested by simulations, led to a significant improvement of leukemia cell growth inhibition and to an increase up to 1 order of magnitude of the affinity toward Abl.

Paclitaxel and docetaxel resistance: molecular mechanisms and development of new generation taxanes.

Taxanes represent one of the most promising classes of anticancer agents. Unfortunately, their clinical success has been limited by the insurgence of cellular resistance, mainly mediated by the expression of the MDR phenotype or by microtubule alterations. However, the remarkable relevance of paclitaxel and docetaxel in clinical oncology stimulated intensive efforts in the last decade to identify new derivatives endowed with improved activities towards resistant tumor cells, resulting in a huge number of novel natural and synthetic taxanes. Among them, several structurally different derivatives were found to exhibit a promising behavior against the MDR phenotype in terms of either MDR inhibiting properties, or enhanced cytotoxicity compared to parental drugs, or both. On the other hand, only in more recent years have the first taxanes retaining activity against resistant cancer cells bearing alterations of the tubulin/microtubule system emerged. This review describes the main molecular mechanisms of resistance to paclitaxel and docetaxel identified so far, focusing on the advances achieved in the development of new taxanes potentially useful for the treatment of resistant tumors.

Inhibition of Bcr-Abl phosphorylation and induction of apoptosis by pyrazolo[3,4-d]pyrimidines in human leukemia cells.

A series of pyrazolo[3,4-d]pyrimidines, previously found to be Src inhibitors, was tested for their ability to inhibit proliferation of three Bcr-Abl-positive human leukemia cell lines (K-562, KU-812, and MEG-01), on the basis of the experimental evidence that various Src inhibitors are also active against Bcr-Abl kinase (the so called dual Src/Abl inhibitors). They reduce Bcr-Abl tyrosine phosphorylation and promote apoptosis of the Bcr-Abl-expressing cells. A cell-free enzymatic assay on isolated c-Abl confirmed that such compounds directly inhibit Abl activity. Finally, molecular modeling simulations were also performed to hypothesize the binding mode of the compounds into the Abl binding site.

Ligand-based virtual screening, parallel solution-phase and microwave-assisted synthesis as tools to identify and synthesize new inhibitors of mycobacterium tuberculosis.

In an attempt to identify new inhibitors of the growth of Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis, a procedure for the generation, design, and screening of a ligand-based virtual library was applied. This used both an in silico protocol centered on a recursive partitioning (RP) model described herein, and a pharmacophoric model for antitubercular agents previously generated by our research group. Two candidates emerged from databases of commercially available compounds, both characterized by a minimum inhibitory concentration (MIC) of 25 microg mL(-1). Based on these compounds, two series of derivatives were synthesized by both parallel solution-phase and microwave-assisted synthesis, leading to enhanced antimycobacterial activity. During both the design and synthesis, attention was focused on the efficient allocation of available resources with the aim of reducing the overall costs associated with calculation and synthesis.

The betaI/betaIII-tubulin isoforms and their complexes with antimitotic agents. Docking and molecular dynamics studies.

Both microtubule destabilizer and stabilizer agents are important molecules in anticancer therapy. In particular, paclitaxel has been demonstrated to be effective for the treatment of ovarian, breast, and nonsmall cell lung carcinomas. It has been shown that emergence of resistance against this agent correlates with an increase in the relative abundance of tubulin isoform betaIII and that the more recently discovered IDN5390 can be effectively used once resistance has emerged. In this paper, we analyze the binding modes of these antimitotic agents to type I and III isoforms of beta-tubulin by computational methods. Our results are able to provide a molecular explanation of the experimental data. Using the same protocol, we could also show that no preference for any of the two isoforms can be detected for epothilone A, a potentially very interesting drug for which no data about the emergence of resistance is currently available. Our analysis provides structural insights about the recognition mode and the stabilization mechanism of these antimitotic agents and provides useful suggestions for the design of more potent and selective antimitotic agents.

A genetic-function-approximation-based QSAR model for the affinity of arylpiperazines toward alpha1 adrenoceptors.

The genetic function approximation (GFA) algorithm has been used to derive a three-term QSAR equation able to correlate the structural properties of arylpiperazine derivatives with their affinity toward the alpha1 adrenoceptor (alpha1-AR). The number of rotatable bonds, the hydrogen-bond properties, and a variable belonging to a topological family of descriptors (chi) showed significant roles in the binding process toward alpha1-AR. The new model was also compared to a previous pharmacophore for alpha1-AR antagonists and a QSAR model for alpha2-AR antagonists with the aim of finding common or different key determinants influencing both affinity and selectivity toward alpha1- and alpha2-AR.