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.

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.

Targeting the Protein Tunnels of the Urease Accessory Complex: A Theoretical Investigation.

Urease is a nickel-containing enzyme that is essential for the survival of several and often deadly pathogenic bacterial strains, including Helicobacter pylori. Notwithstanding several attempts, the development of direct urease inhibitors without side effects for the human host remains, to date, elusive. The recently solved X-ray structure of the HpUreDFG accessory complex involved in the activation of urease opens new perspectives for structure-based drug discovery. In particular, the quaternary assembly and the presence of internal tunnels for nickel translocation offer an intriguing possibility to target the HpUreDFG complex in the search of indirect urease inhibitors. In this work, we adopted a theoretical framework to investigate such a hypothesis. Specifically, we searched for putative binding sites located at the protein-protein interfaces on the HpUreDFG complex, and we challenged their druggability through structure-based virtual screening. We show that, by virtue of the presence of tunnels, some protein-protein interfaces on the HpUreDFG complex are intrinsically well suited for hosting small molecules, and, as such, they possess good potential for future drug design endeavors.

Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics.

Computational approaches currently assist medicinal chemistry through the entire drug discovery pipeline. However, while several computational tools and strategies are available to predict binding affinity, predicting the drug-target binding kinetics is still a matter of ongoing research. Here, we challenge scaled molecular dynamics simulations to assess the off-rates for a series of structurally diverse inhibitors of the heat shock protein 90 (Hsp90) covering 3 orders of magnitude in their experimental residence times. The derived computational predictions are in overall good agreement with experimental data. Aside from the estimation of exit times, unbinding pathways were assessed through dimensionality reduction techniques. The data analysis framework proposed in this work could lead to better understanding of the mechanistic aspects related to the observed kinetic behavior.

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-3ß 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.

Binding Residence Time through Scaled Molecular Dynamics: A Prospective Application to hDAAO Inhibitors.

Traditionally, a drug potency is expressed in terms of thermodynamic quantities, mostly Kd, and empirical IC50 values. Although binding affinity as an estimate of drug activity remains relevant, it is increasingly clear that it is also important to include (un)binding kinetic parameters in the characterization of potential drug-like molecules. Herein, we used standard in silico screening to identify a series of structurally related inhibitors of hDAAO, a flavoprotein involved in schizophrenia and neuropathic pain. We applied a novel methodology, based on scaled molecular dynamics, to rank them according to their residence times. Notably, we challenged the application in a prospective fashion for the first time. The good agreement between experimental residence times and the predicted residence times highlighted the procedure's reliability in both predictive and refinement scenarios. Additionally, through further inspection of the performed simulations, we substantiated a previous hypothesis on the involvement of a protein loop during ligand unbinding.

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.

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.

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.

Revealing DNA interactions with exogenous agents by surface-enhanced Raman scattering.

The standard protocols for DNA analysis largely involve polymerase chain reaction (PCR). However, DNA structures bound to chemical agents cannot be PCR-amplified, and therefore any sequence changes induced by external agents may be neglected. Thus, the development of analytical tools capable of characterizing the biochemical mechanisms associated with chemically induced DNA damage is demanded for the rational design of more effective chemotherapy drugs, understanding the mode of actions of carcinogenic chemicals, and monitoring the genotypic toxicology of environments. Here we report a fast, high-throughput, low-cost method for the characterization and quantitative recognition of DNA interactions with exogenous agents based on surface-enhanced Raman scattering spectroscopy. As representative chemical agents, we selected a chemotherapeutic drug (cisplatin) which forms covalent adducts with DNA, a duplex intercalating agent (methylene blue), and a cytotoxic metal ion (Hg(II)) which inserts into T:T mismatches. Rich structural information on the DNA complex architecture and properties is provided by the unique changes of their SERS spectra, which also offer an efficient analytical tool to quantify the extent of such binding.

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.

An automated docking protocol for hERG channel blockers.

A docking protocol aimed at obtaining a consistent qualitative and quantitative picture of binding for a series of hERG channel blockers is presented. To overcome the limitations experienced by standard procedures when docking blockers at hERG binding site, we designed a strategy that explicitly takes into account the conformations of the channel, their possible intrinsic symmetry, and the role played by the configurational entropy of ligands. The protocol was developed on a series of congeneric sertindole derivatives, allowing us to satisfactorily explain the structure-activity relationships for this set of blockers. In addition, we show that the performance of structure-based models relying on multiple-receptor conformations statistically increases when the protein conformations are chosen in such a way as to capture relevant structural features at the binding site. The protocol was then successfully applied to a series of structurally unrelated blockers.

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.

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.

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.

Computational drug discovery under RNA times.

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.

Computational investigation of the binding mode of bis(hydroxylphenyl)arenes in 17ß-HSD1: molecular dynamics simulations, MM-PBSA free energy calculations, and molecular electrostatic potential maps.

17ß-Hydroxysteroid dehydrogenase type 1 (17ß-HSD1) catalyzes the last step of the estrogen biosynthesis, namely the reduction of estrone to the biologically potent estradiol. As such it is a potentially attractive drug target for the treatment of estrogen-dependent diseases like breast cancer and endometriosis. 17ß-HSD1 belongs to the bisubstrate enzymes and exists as an ensemble of conformations. These principally differ in the region of the ßFαG'-loop, suggesting a prominent role in substrate and inhibitor binding. Although several classes of potent non-steroidal 17ß-HSD1 inhibitors currently exist, their binding mode is still unclear. We aimed to elucidate the binding mode of bis(hydroxyphenyl)arenes, a highly potent class of 17ß-HSD1 inhibitors, and to rank these compounds correctly with respect to their inhibitory potency, two essential aspects in drug design. Ensemble docking experiments resulted in a steroidal binding mode for the closed enzyme conformations and in an alternative mode for the opened and occluded conformers with the inhibitors placed below the NADPH interacting with it synergically via π-π stacking and H-bond formation. Both binding modes were investigated by MD simulations and MM-PBSA binding free energy estimations using as representative member for this class compound 1 (50 nM). Notably, only the alternative binding mode proved stable and was energetically more favorable, while when simulated in the steroidal binding mode compound 1 was displaced from the active site. In parallel, ab initio studies of small NADPH-inhibitor complexes were performed, which supported the importance of the synergistic interaction between inhibitors and cofactor.

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.