Innovative Strategy toward Mutant CFTR Rescue in Cystic Fibrosis: Design and Synthesis of Thiadiazole Inhibitors of the E3 Ligase RNF5

In cystic fibrosis (CF), deletion of phenylalanine 508 (F508del) in the CF transmembrane conductance regulator (CFTR) is associated to misfolding and defective gating of the mutant channel. One of the most promising CF drug targets is the ubiquitin ligase RNF5, which promotes F508del-CFTR degradation. Recently, the first ever reported inhibitor of RNF5 was discovered, i.e., the 1,2,4-thiadiazol-5-ylidene inh-2. Here, we designed and synthesized a series of new analogues to explore the structure-activity relationships (SAR) of this class of compounds. SAR efforts ultimately led to compound 16, which showed a greater F508del-CFTR corrector activity than inh-2, good tolerability, and no toxic side effects. Analogue 16 increased the basal level of autophagy similar to what has been described with RNF5 silencing. Furthermore, co-treatment with 16 significantly improved the F508del-CFTR rescue induced by the triple combination elexacaftor/tezacaftor/ivacaftor in CFBE41o^- cells. These findings validate the 1,2,4-thiadiazolylidene scaffold for the discovery of novel RNF5 inhibitors and provide evidence to pursue this unprecedented strategy for the treatment of CF.

Network modeling helps to tackle the complexity of drug-disease systems

From the (patho)physiological point of view, diseases can be considered as emergent properties of living systems stemming from the complexity of these systems. Complex systems display some typical features, including the presence of emergent behavior and the organization in successive hierarchic levels. Drug treatments increase this complexity scenario, and from some years the use of network models has been introduced to describe drug-disease systems and to make predictions about them with regard to several aspects related to drug discovery. Here, we review some recent examples thereof with the aim to illustrate how network science tools can be very effective in addressing both tasks. We will examine the use of bipartite networks that lead to the important concept of "disease module", as well as the introduction of more articulated models, like multi-scale and multiplex networks, able to describe disease systems at increasing levels of organization. Examples of predictive models will then be discussed, considering both those that exploit approaches purely based on graph theory and those that integrate machine learning methods. A short account of both kinds of methodological applications will be provided. Finally, the point will be made on the present situation of modeling complex drug-disease systems highlighting some open issues. This article is categorized under: Neurological Diseases > Computational Models Infectious Diseases > Computational Models Cardiovascular Diseases > Computational Models.

COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19

The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet ( http://compmedchem.unibo.it/covidrugnet ), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.

Probing the transport of Ni(II) ions through the internal tunnels of the Helicobacter pylori UreDFG multimeric protein complex

The survival of several pathogenic bacteria, such as Helicobacter pylori (Hp), relies on the activity of the nickel-dependent enzyme urease. Nickel insertion into urease is mediated by a multimeric chaperone complex (HpUreDFG) that is responsible for the transport of Ni(II) from a conserved metal binding motif located in the UreG dimer (CPH motif) to the catalytic site of the enzyme. The X-ray structure of HpUreDFG revealed the presence of water-filled tunnels that were proposed as a route for Ni(II) translocation. Here, we probe the transport of Ni(II) through the internal tunnels of HpUreDFG, from the CPH motif to the external surface of the complex, using microsecond-long enhanced molecular dynamics simulations. The results suggest a "bucket-brigade" mechanism whereby Ni(II) can be transported through a series of stations found along these internal pathways.

Targeting the JAK/STAT Pathway: A Combined Ligand- and Target-Based Approach

Janus kinases (JAKs) are a family of proinflammatory enzymes able to mediate the immune responses and the inflammatory cascade by modulating multiple cytokine expressions as well as various growth factors. In the present study, the inhibition of the JAK-signal transducer and activator of transcription (STAT) signaling pathway is explored as a potential strategy for treating autoimmune and inflammatory disorders. A computationally driven approach aimed at identifying novel JAK inhibitors based on molecular topology, docking, and molecular dynamics simulations was carried out. For the best candidates selected, the inhibitory activity against JAK2 was evaluated in vitro. Two hit compounds with a novel chemical scaffold, 4 (IC₅₀ = 0.81 μM) and 7 (IC₅₀ = 0.64 μM), showed promising results when compared with the reference drug Tofacitinib (IC₅₀ = 0.031 μM).

Conduction and Gating Properties of the TRAAK Channel from Molecular Dynamics Simulations with Different Force Fields

In recent years, the K2P family of potassium channels has been the subject of intense research activity. Owing to the complex function and regulation of this family of ion channels, it is common practice to complement experimental findings with the atomistic description provided by computational approaches such as molecular dynamics (MD) simulations, especially, in light of the unprecedented timescales accessible at present. However, despite recent substantial improvements, the accuracy of MD simulations is still undermined by the intrinsic limitations of force fields. Here, we systematically assessed the performance of the most popular force fields employed to study ion channels at timescales that are orders of magnitude greater than the ones accessible when these energy functions were first developed. Using 32 μs of trajectories, we investigated the dynamics of a member of the K2P ion channel family, the TRAAK channel, using two established force fields in simulations of biological systems: AMBER and CHARMM. We found that while results are comparable on the nanosecond timescales, significant inconsistencies arise at microsecond timescales.

Drug Research Meets Network Science: Where Are We?

Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.

Exploring Conformational Dynamics of the Extracellular Venus flytrap Domain of the GABAB Receptor: A Path-Metadynamics Study

γ-Aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the central nervous system (CNS). Dysfunctional GABAergic neurotransmission is associated with numerous neurological and neuropsychiatric disorders. The GABAB receptor (GABAB-R) is a heterodimeric class C G protein-coupled receptor (GPCR) comprised of GABAB1a/b and GABAB2 subunits. The orthosteric binding site for GABA is located in the extracellular Venus flytrap (VFT) domain of the GABAB1a/b. Knowledge about molecular mechanisms and druggable receptor conformations associated with activation is highly important to understand the receptor function and for rational drug design. Currently, the conformational changes of the receptor upon activation are not well described. On the basis of other class C members, the VFT is proposed to fluctuate between an open/inactive and closed/active state and one of these conformations is stabilized upon ligand binding. In the present study, we investigated the dynamics of the GABAB1b-R VFT in the apo form by combining unbiased molecular dynamics with path-metadynamics. Our simulations confirmed the open/inactive and closed/active state as the main conformations adopted by the receptor. Sizeable energy barriers were found between stable minima, suggesting a relatively slow interconversion. Previously undisclosed metastable states were also identified, which might hold potential for future drug discovery efforts.

Multi-target dopamine D3 receptor modulators: Actionable knowledge for drug design from molecular dynamics and machine learning

Local changes in the structure of G-protein coupled receptors (GPCR) binders largely affect their pharmacological profile. While the sought efficacy can be empirically obtained by introducing local modifications, the underlining structural explanation can remain elusive. Here, molecular dynamics (MD) simulations of the eticlopride-bound inactive state of the Dopamine D3 Receptor (D3DR) have been clustered using a machine learning-based approach in the attempt to rationalize the efficacy change in four congeneric modulators. Accumulating extended MD trajectories of receptor-ligand complexes, we observed how the increase in ligand flexibility progressively destabilized the crystal structure of the inactivated receptor. To prospectively validate this model, a partial agonist was rationally designed based on structural insights and computational modeling, and eventually synthesized and tested. Results turned out to be in line with the predictions. This case study suggests that the investigation of ligand flexibility in the framework of extended MD simulations can assist and inform drug design strategies, highlighting its potential role as a powerful in silico counterpart to functional assays.

Fully Flexible Docking via Reaction-Coordinate-Independent Molecular Dynamics Simulations

Predicting the geometry of protein-ligand binding complexes is of primary importance for structure-based drug discovery. Molecular dynamics (MD) is emerging as a reliable computational tool for use in conjunction with, or an alternative to, docking methods. However, simulating the protein-ligand binding process often requires very expensive simulations. This drastically limits the practical application of MD-based approaches. Here, we propose a general framework to accelerate the generation of putative protein-ligand binding modes using potential-scaled MD simulations. The proposed dynamical protocol has been applied to two pharmaceutically relevant systems (GSK-3β and the N-terminal domain of HSP90α). Our approach is fully independent of any predefined reaction coordinate (or collective variable). It identified the correct binding mode of several ligands and can thus save valuable computational time in dynamic docking simulations.

Synthesis of natural urolithin M6, a galloflavin mimetic, as a potential inhibitor of lactate dehydrogenase A

Glycolysis is the main route for energy production in tumors. LDH-A is a key enzyme of this process and its inhibition represents an attractive strategy to hamper cancer cell metabolism. Galloflavin is a reliable LDH-A inhibitor as previously identified by us; however, its poor physicochemical properties and chemical tractability render it unsuitable for further development. Therefore, a rational design was undertaken with the aim to reproduce the pharmacophore of galloflavin on simpler, potentially more soluble and synthetic accessible scaffolds. Following a process of structural simplification, natural urolithin M6 (UM6), which is an ellagitannin metabolite produced by gut microbiota, was identified as a putative galloflavin mimetic. In the present study, the synthesis of UM6 is described for the first time. An efficient synthetic pathway has been developed, which involved five steps from readily accessible starting materials. The key reaction steps, a Suzuki coupling and an intramolecular C-H oxygenation, have been optimized to improve the synthetic feasibility and provide the best conditions in terms of reaction time and yield. Moreover, this route would be suitable to obtain other analogs for SAR studies. Preliminary biological tests revealed that UM6 was able to smoothly reproduce the behavior of galloflavin, confirming that our approach was successful in providing a new and accessible structure in the search for new LDH-A inhibitors.

Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.

Development of a multisite model for Ni(II) ion in solution from thermodynamic and kinetic data

Force-field parameters are developed for a multisite model of Ni(II) ions to be used in molecular dynamics simulations combined to enhanced sampling methods. The performances of two charge-partitioning schemes are validated by taking into account structural, thermodynamic, and kinetic observables. One of the two models, featuring partial charges on the dummy atoms only, matches both Ni(II) free energy of solvation and water exchange rates. Such model is particularly suited to study complexation events at a fully dynamic description. © 2017 Wiley Periodicals, Inc.

Protein Tunnels: The Case of Urease Accessory Proteins

Transition metals are both essential micronutrients and limited in environmental availability. The Ni(II)-dependent urease protein, the most efficient enzyme known to date, is a paradigm for studying the strategies that cells use to handle an essential, yet toxic, metal ion. Urease is a virulence factor of several human pathogens, in addition to decreasing the efficiency of soil organic nitrogen fertilization. Ni(II) insertion in the urease active site is performed through the action of three essential accessory proteins: UreD, UreF, and UreG. The crystal structure of the UreD-UreF-UreG complex from the human pathogen Helicobacter pylori (HpUreDFG) revealed the presence of tunnels that cross the entire length of both UreF and UreD, potentially able to deliver Ni(II) ions from UreG to apo-urease. Atomistic molecular dynamics simulations performed on the HpUreDFG complex in explicit solvent and at physiological ionic conditions demonstrate the stability of these protein tunnels in solution and provide insights on the trafficking of water molecules inside the tunnels. The presence of different alternative routes across the identified tunnels for Ni(II) ions, water molecules, and carbonate ions, all involved in urease activation, is highlighted here, and their potential role in the urease activation mechanism is discussed.

Mapping Cholesterol Interaction Sites on Serotonin Transporter through Coarse-Grained Molecular Dynamics

Serotonin transporter (SERT) modulates serotonergic signaling via re-uptake of serotonin in pre-synaptic cells. The inclusion in cholesterol-enriched membrane domains is crucial for SERT activity, suggesting a cross-talk between the protein and the sterol. Here, we develop a protocol to identify potential cholesterol interaction sites coupling statistical analysis to multi-microsecond coarse-grained molecular dynamics simulations of SERT in a previously validated raft-like membrane model. Six putative sites were found, including a putative CRAC motif on TM4 and a CARC motif on TM10. Among them, four hot-spots near regions related to ion binding, transport, and inhibition were detected. Our results encourage prospective studies to unravel mechanistic features of the transporter and related drug discovery implications.

Lactate dehydrogenase inhibitors can reverse inflammation induced changes in colon cancer cells

The inflammatory microenvironment is an essential component of neoplastic lesions and can significantly impact on tumor progression. Besides facilitating invasive growth, inflammatory cytokines were also found to reprogram cancer cell metabolism and to induce aerobic glycolysis. Previous studies did not consider the possible contribution played in these changes by lactate dehydrogenase (LDH). The A isoform of LDH (LDH-A) is the master regulator of aerobic glycolysis; it actively reduces pyruvate and causes enhanced lactate levels in tumor tissues. In cancer cells, lactate was recently found to directly increase migration ability; moreover, when released in the microenvironment, it can facilitate matrix remodeling. In this paper, we illustrate that treatment of human colon adenocarcinoma cells with TNF-α and IL-17, two pro-inflammatory cytokines, modifies LDH activity, causing a shift toward the A isoform which results in increased lactate production. At the same time, the two cytokines appeared to induce features of epithelial-mesenchymal transition in the treated cells, such as reduction of E-cadherin levels and increased secretion of metalloproteinases. Noteworthy, oxamate and galloflavin, two inhibitors of LDH activity which reduce lactate production in cells, were found to relieve the inflammation-induced effects. These results suggest LDH-A and/or lactate as common elements at the cross-road between cancer cell metabolism, tumor progression and inflammation. At present, LDH inhibitors suitable for clinical use are actively searched as possible anti-proliferative agents; our data lead to hypothesize for these compounds a wider potential in anticancer treatment.

Atomic-Level Characterization of the Chain-Flipping Mechanism in Fatty-Acids Biosynthesis

During fatty acids biosynthesis the elongating acyl chain is sequestered within the core of the highly conserved acyl carrier protein (ACP). At each catalytic step, the acyl intermediates are transiently delivered from ACP to the active site of the enzymatic counterparts and, at the same time, are protected from the solvent to prevent nonselective reactivity. Yet, the molecular determinants of such a universal transition-termed chain flipping-remain poorly understood. Here we capture the atomic-level details of the chain-flipping mechanism by using metadynamics simulations. We observe the fatty-acid chain gliding through the protein-protein interface with barely 30% of its surface exposed to water molecules. The small ACP's helix III acts as gatekeeper of the process, and we find its conformational plasticity critical for a successful substrate transfer. The results are in agreement with a wide range of experimental observations and provide unprecedented insight on the molecular determinants and driving forces of the chain-flipping process.

A ligand-based virtual screening approach to identify small molecules as HERG channel activators

The hERG potassium channel is currently emerging as a potential target for the treatment of some forms of arrhythmias or to contrast an unintentional channel block caused by drugs. Despite its therapeutic relevance, so far only few compounds are described as able to enhance channel function by potentiating hERG currents. This gap is also related to the lack of hERG crystal structure which strongly limits the possibility to employ structure-based techniques in the search and design of novel activators. To overcome this limitation, in the present work, a ligand-based virtual screening was performed using as separate search queries two conformations of NS1643, the most deeply investigated and better characterized hERG activator. The library of compounds resulting from the virtual screening was then clustered based on recurring chemical features, and 5 hits were selected to be evaluated for their ability to enhance hERG current in vitro. Compound 3 showed a good activating effect, also displaying a mechanism of action similar to that of NS1643. Moreover, the most interesting compounds were further investigated by synthesizing in a parallel fashion some analogs, with the aim to get insights about structure-activity relationships.

Protein Flexibility in Drug Discovery: From Theory to Computation

Nowadays it is widely accepted that the mechanisms of biomolecular recognition are strongly coupled to the intrinsic dynamic of proteins. In past years, this evidence has prompted the development of theoretical models of recognition able to describe ligand binding assisted by protein conformational changes. On a different perspective, the need to take into account protein flexibility in structure-based drug discovery has stimulated the development of several and extremely diversified computational methods. Herein, on the basis of a parallel between the major recognition models and the simulation strategies used to account for protein flexibility in ligand binding, we sort out and describe the most innovative and promising implementations for structure-based drug discovery.

Modeling lipid raft domains containing a mono-unsaturated phosphatidylethanolamine species

An advanced coarse-grained model for “atypical” lipid rafts was built and validated to be employed in studies of membrane-protein interactions.

Phytoestrogens in postmenopause: the state of the art from a chemical, pharmacological and regulatory perspective

Phytoestrogens represent a diverse group of non-steroidal natural products, which seem to have some oestrogenic effects and are often marketed as food supplements. Population exposed to phytoestrogens is potentially increasing, in part because an unfavourable risk-benefit profile of Hormone Replacement Therapy (HRT) for prolonged treatments (e.g., osteoporosis prevention) highlighted by the publication of the Women Health Initiative (WHI) trial in 2002, but also because many post-menopausal women often perceived phytoestrogens in food supplements as a safer alternative than HRT. Despite of increasing preclinical and clinical studies in the past decade, appealing evidence is still lacking to support the overall positive risk-benefit profile of phytoestrogens. Their status as food supplements seems to discourage studies to obtain new evidence, and the chance to buy them by user's initiative make it difficult to survey their prevalence and pattern of use. The aim of the present review is to: (a) outline the clinical scenario underlying the increased interest on phytoestrogens, by overviewing the evolution of the evidence on HRT and its main therapeutic goals (e.g., menopausal symptoms relief, chemoprevention, osteoporosis prevention); (b) address the chemical and pharmacological features (e.g. chemical structure, botanical sources, mechanism of action) of the main compounds (e.g., isoflavones, lignans, coumestans); (c) describe the clinical evidence on potential therapeutic applications; (d) put available evidence on their riskbenefit profile in a regulatory perspective, in light of the recent regulation on health claims of food supplements.

Novel antiproliferative chimeric compounds with marked histone deacetylase inhibitory activity

Given our interest in finding potential antitumor agents and in view of the multifactorial mechanistic nature of cancer, in the present work, taking advantage of the multifunctional ligands approach, new chimeric molecules were designed and synthesized by combining in single chemical entities structural features of SAHA, targeting histone deacetylases (HDACs), with substituted stilbene or terphenyl derivatives previously obtained by us and endowed with antiproliferative and pro-apoptotic activity. The new chimeric derivatives were characterized with respect to their cytotoxic activity and their effects on cell cycle progression on different tumor cell lines, as well as their HDACs inhibition. Among the other, trans -6 showed the most interesting biological profile, as it exhibited a strong pro-apoptotic activity in tumor cell lines in comparison with both of its parent compounds and a marked HDAC inhibition.

Mechanistic insights into Pin1 peptidyl-prolyl cis-trans isomerization from umbrella sampling simulations

The peptidyl-proyl isomerase Pin1 plays a key role in the regulation of phospho(p)-Ser/Thr-Pro proteins, acting as a molecular timer of the cell cycle. After recognition of these motifs, Pin1 catalyzes the rapid cis-trans isomerization of proline amide bonds of substrates, contributing to maintain the equilibrium between the two conformations. Although a great interest has arisen on this enzyme, its catalytic mechanism has long been debated. Here, the cis-trans isomerization of a model peptide system was investigated by means of umbrella sampling simulations in the Pin1-bound and unbound states. We obtained free energy barriers consistent with experimental data, and identified several enzymatic features directly linked to the acceleration of the prolyl bond isomerization. In particular, an enhanced autocatalysis, the stabilization of perturbed ground state conformations, and the substrate binding in a procatalytic conformation were found as main contributions to explain the lowering of the isomerization free energy barrier.

Protein dynamics of the HIF-2α PAS-B domain upon heterodimerization and ligand binding

Hypoxia-Inducible Factor (HIF) transcription factors are heterodimeric proteins involved in the regulation of oxygen homeostatis. Their upregulation has been related to several tumors with a remarkably poor clinical outcome. The recent discovery of a druggable cavity in the HIF-2α PAS-B domain has opened an unprecedented opportunity for targeting the HIF-2α transcription factor in view of pharmaceutical strategies. Coincidentally, a novel compound able to selectively disrupt the HIF heterodimerization with a submicromolar activity has been reported. In this work, we investigated the molecular mechanisms responsible for the inhibition by comparing the dynamical features of the HIF-2α PAS-B monomer and the HIF-2α PAS-B/HIF-1β PAS-B complex, in the ligand-bound and -unbound states. Plain and biased Molecular Dynamics were used to characterize the differential conformational changes both structurally and energetically.

Collecting and assessing human lactate dehydrogenase-A conformations for structure-based virtual screening

Human lactate dehydrogenase-A (LDHA) is emerging as a promising anticancer target. Up to now, structure-based investigations for identifying inhibitors of this enzyme have not explicitly accounted for active site flexibility. In the present study, by combining replica exchange molecular dynamics with network and cluster analyses, we identified reliable LDHA conformations for structure-based ligand design. The selected conformations were challenged and validated by retrospective virtual screening simulations.

Design, synthesis, and biological and crystallographic evaluation of novel inhibitors of Plasmodium falciparum enoyl-ACP-reductase (PfFabI)

Malaria, a disease of worldwide significance, is responsible for over one million deaths annually. The liver-stage of Plasmodium's life cycle is the first, obligatory, but clinically silent step in malaria infection. The P. falciparum type II fatty acid biosynthesis pathway (PfFAS-II) has been found to be essential for complete liver-stage development and has been regarded as a potential antimalarial target for the development of drugs for malaria prophylaxis and liver-stage eradication. In this paper, new coumarin-based triclosan analogues are reported and their biological profile is explored in terms of inhibitory potency against enzymes of the PfFAS-II pathway. Among the tested compounds, 7 and 8 showed the highest inhibitory potency against Pf enoyl-ACP-reductase (PfFabI), followed by 15 and 3. Finally, we determined the crystal structures of compounds 7 and 11 in complex with PfFabI to identify their mode of binding and to confirm outcomes of docking simulations.

A natural-like synthetic small molecule impairs bcr-abl signaling cascades and induces megakaryocyte differentiation in erythroleukemia cells

Over the past years, we synthesized a series of new molecules that are hybrids of spirocyclic ketones as complexity-bearing cores with bi- and ter-phenyls as privileged fragments. Some of these newly-shaped small molecules showed antiproliferative, pro-apoptotic and differentiating activity in leukemia cell lines. In the present study, to investigate more in depth the mechanisms of action of these molecules, the protein expression profiles of K562 cells treated with or without the compounds IND_S1, MEL_T1, IND_S7 and MEL_S3 were analyzed using two-dimensional gel electrophoresis coupled with mass spectrometry. Proteome comparisons revealed several differentially expressed proteins, mainly related to cellular metabolism, chaperone activity, cytoskeletal organization and RNA biogenesis. The major results were validated by Western blot and qPCR. To attempt integrating findings into a cellular signaling context, proteomic data were explored using MetaCore. Network analysis highlighted relevant relationships between the identified proteins and additional potential effectors. Notably, qPCR validation of central hubs showed that the compound MEL_S3 induced high mRNA levels of the transcriptional factors EGR1 and HNF4-alpha; the latter to our knowledge is reported here for the first time to be present in K562 cells. Consistently with the known EGR1 involvement in the regulation of differentiation along megakaryocyte lineage, MEL_S3-treated leukemia cells showed a marked expression of glycoprotein IIb/IIIa (CD41) and glycoprotein Ib (CD42), two important cell markers in megakaryocytic differentiation, together with morphological aspects of megakaryoblasts and megakaryocytes.

Ion conduction through the hERG potassium channel

The inward rectifier voltage-gated potassium channel hERG is of primary importance for the regulation of the membrane potential of cardiomyocytes. Unlike most voltage-gated K⁺-channels, hERG shows a low elementary conductance at physiological voltage and potassium concentration. To investigate the molecular features underlying this unusual behavior, we simulated the ion conduction through the selectivity filter at a fully atomistic level by means of molecular dynamics-based methods, using a homology-derived model. According to our calculations, permeation of potassium ions can occur along two pathways, one involving site vacancies inside the filter (showing an energy barrier of about 6 kcal mol⁻¹), and the other characterized by the presence of a knock-on intermediate (about 8 kcal mol⁻¹). These barriers are indeed in accordance with a low conductance behavior, and can be explained in terms of a series of distinctive structural features displayed by the hERG ion permeation pathway.

Novel 1,3-dipropyl-8-(3-benzimidazol-2-yl-methoxy-1-methylpyrazol-5-yl)xanthines as potent and selective A₂B adenosine receptor antagonists

Molecular modeling studies, including the comparative molecular field analysis (CoMFA) method, on 52 antagonists of the A(2B) adenosine receptor with known biological activity were performed to identify the three-dimensional features responsible for A(2B) adenosine receptor antagonist activity. On the basis of these and previous results on the potent antagonist effect of 8-pyrazolyl-xanthines at human A(2B)AR, a new series of compounds was synthesized and evaluated in binding studies against the human A(1), A(2A), A(3), and A(2B)ARs. A remarkable improvement in selectivity with respect to the previous series, maintaining the potency at human A(2B) receptor, was achieved, as exemplified by the 8-[3-(4-chloro-6-trifluoromethyl-1H-benzoimidazol-2-yl-methoxy)-1-methyl-1H-pyrazol-5-yl]-1,3-dipropyl-3,7-dihydro-purine-2,6-dione derivative 66: K(i) A(2B) = 9.4 nM, IC(50) hA(2B) = 26 nM hA(1)/hA(2B) = 269, hA(2A)/hA(2B) > 106, hA(3)/hA(2B) >106. This study also led to the identification of a series of pyrazole-xanthine compounds with a simplified structure, exemplified by 8-(3-hydroxy-1-methyl-1H-pyrazol-5-yl)-xanthine 80 displaying very high affinity at A(2B)AR with good selectivity over AR subtypes (K(i) = 4.0 nM, IC(50) hA(2B) = 20 nM hA(1)/hA(2B) = 183, hA(2A),hA(3)/hA(2B) > 250).

Galloflavin (CAS 568-80-9): a novel inhibitor of lactate dehydrogenase

One of the most prominent alterations in cancer cells is their strict dependence on the glycolytic pathway for ATP generation. This observation led to the evaluation of glycolysis inhibitors as potential anticancer agents. The inhibition of lactate dehydrogenase (LDH) is a promising way to inhibit tumor cell glucose metabolism without affecting the energetic balance of normal tissues. However, the success of this approach depends chiefly on the availability of inhibitors that display good selectivity. We identified a compound (galloflavin, CAS 568-80-9) which, in contrast to other inhibitors of human LDH, hinders both the A and B isoforms of the enzyme. To determine the mechanism of action, we collected LDH-A and -B inhibition data in competition reactions with pyruvate or NADH and evaluated the results using software for enzyme kinetics analysis. We found that galloflavin inhibits both human LDH isoforms by preferentially binding the free enzyme, without competing with the substrate or cofactor. The calculated Ki values for pyruvate were 5.46 μM (LDH-A) and 15.06 μM (LDH-B). In cultured tumor cells, galloflavin blocked aerobic glycolysis at micromolar concentrations, did not interfere with cell respiration, and induced cell death by triggering apoptosis. To our knowledge, the inhibition of LDH is, to date, the only biochemical effect described for galloflavin. Because galloflavin is not commercially available, we also describe herein a procedure for its synthesis and report its first full chemical characterization.

Substrate binding process and mechanistic functioning of type 1 11β-hydroxysteroid dehydrogenase from enhanced sampling methods

In humans, type 1 11β-hydroxysteroid dehydrogenase (11β-HSD-1) plays a key role in the regulation of the glucocorticoids balance by converting the inactive hormone cortisone into cortisol. Numerous functional aspects of 11β-HSD-1 have been understood thanks to the availability at the Worldwide Protein Data Bank of a number of X-ray structures of the enzyme either alone or in complex with inhibitors, and to several experimental data. However at present, a complete description of the dynamic behaviour of 11β-HSD-1 upon substrate binding is missing. To this aim we firstly docked cortisone into the catalytic site of 11β-HSD-1 (both wild type and Y177A mutant), and then we used steered molecular dynamics and metadynamics to simulate its undocking. This methodology helped shedding light at molecular level on the complex relationship between the enzyme and its natural substrate. In particular, the work highlights a) the reason behind the functional dimerisation of 11β-HSD-1, b) the key role of Y177 in the cortisone binding event, c) the fine tuning of the active site degree of solvation, and d) the role of the S228-P237 loop in ligand recognition.

The role of fragment-based and computational methods in polypharmacology

Polypharmacology-based strategies are gaining increased attention as a novel approach to obtaining potentially innovative medicines for multifactorial diseases. However, some within the pharmaceutical community have resisted these strategies because they can be resource-hungry in the early stages of the drug discovery process. Here, we report on fragment-based and computational methods that might accelerate and optimize the discovery of multitarget drugs. In particular, we illustrate that fragment-based approaches can be particularly suited for polypharmacology, owing to the inherent promiscuous nature of fragments. In parallel, we explain how computer-assisted protocols can provide invaluable insights into how to unveil compounds theoretically able to bind to more than one protein. Furthermore, several pragmatic aspects related to the use of these approaches are covered, thus offering the reader practical insights on multitarget-oriented drug discovery projects.

Sequential virtual screening approach to the identification of small organic molecules as potential BACE-1 inhibitors

In this letter, we report on the sequential application of two different in silico screening approaches combined with bioassays aimed at the identification of small organic molecules as potential BACE-1 inhibitors. Two hits endowed of micromolar inhibitory potency were selected, and the binding mode of the most potent compound was further characterized through docking simulations.

Insights in 17beta-HSD1 enzyme kinetics and ligand binding by dynamic motion investigation

BACKGROUND

Bisubstrate enzymes, such as 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), exist in solution as an ensemble of conformations. 17beta-HSD1 catalyzes the last step of the biosynthesis of estradiol and, thus, it is a potentially attractive target for breast cancer treatment.

METHODOLOGY/PRINCIPAL FINDINGS

To elucidate the conformational transitions of its catalytic cycle, a structural analysis of all available crystal structures was performed and representative conformations were assigned to each step of the putative kinetic mechanism. To cover most of the conformational space, all-atom molecular dynamic simulations were performed using the four crystallographic structures best describing apoform, opened, occluded and closed state of 17beta-HSD1 as starting structures. With three of them, binary and ternary complexes were built with NADPH and NADPH-estrone, respectively, while two were investigated as apoform. Free energy calculations were performed in order to judge more accurately which of the MD complexes describes a specific kinetic step.

CONCLUSIONS/SIGNIFICANCE

Remarkably, the analysis of the eight long range trajectories resulting from this multi-trajectory/-complex approach revealed an essential role played by the backbone and side chain motions, especially of the betaF alphaG'-loop, in cofactor and substrate binding. Thus, a selected-fit mechanism is suggested for 17beta-HSD1, where ligand-binding induced concerted motions of the FG-segment and the C-terminal part guide the enzyme along its preferred catalytic pathway. Overall, we could assign different enzyme conformations to the five steps of the random bi-bi kinetic cycle of 17beta-HSD1 and we could postulate a preferred pathway for it. This study lays the basis for more-targeted biochemical studies on 17beta-HSD1, as well as for the design of specific inhibitors of this enzyme. Moreover, it provides a useful guideline for other enzymes, also characterized by a rigid core and a flexible region directing their catalysis.

Identification of biphenyl-based hybrid molecules able to decrease the intracellular level of Bcl-2 protein in Bcl-2 overexpressing leukemia cells

With the aim of enhancing the structural complexity and diversity of an existing collection of bi- and terphenyl compounds, we synthesized hybrid molecules comprising of spirocyclic ketones (a complexity-bearing core) and bi/terphenyls (privileged fragments). Compounds 1, 3, 4, and 6 showed well-defined activity on apoptosis and differentiation, making them potential leads for development as new anticancer agents and chemical probes to study signaling networks in neoplastic cells.

hERG-related drug toxicity and models for predicting hERG liability and QT prolongation

BACKGROUND

hERG K(+) channels have been recognized as a primary antitarget in safety pharmacology. Their blockade, caused by several drugs with different therapeutic indications, may lead to QT prolongation and, eventually, to potentially fatal arrhythmia, namely torsade de pointes. Therefore, a number of preclinical models have been developed to predict hERG liability early in the drug development process.

OBJECTIVE

The aim of this review is to outline the present state of the art on drug-induced hERG blockade, providing insights on the predictive value of in vitro and in silico models for hERG liability.

METHODS

On the basis of latest reports, high-throughput preclinical models have been discussed outlining advantages and limitations.

CONCLUSION

Although no single model has an absolute value, an integrated risk assessment is recommended to predict the pro-arrhythmic risk of a given drug. This prediction requires expertise from different areas and should encompass emerging issues such as interference with hERG trafficking and QT shortening.