Consensus Scoring Approach To Identify the Inhibitors of AMP-Activated Protein Kinase α2 with Virtual Screening

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

Towards Effective Consensus Scoring in Structure-Based Virtual Screening

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein-ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository ( http://dude.docking.org/ ) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand-protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning.

The Inhibition of Zinc Excitotoxicity and AMPK Phosphorylation by a Novel Zinc Chelator, 2G11, Ameliorates Neuronal Death Induced by Global Cerebral Ischemia

AMP-activated protein kinase (AMPK) is necessary for maintaining a positive energy balance and essential cellular processes such as glycolysis, gene transcription, glucose uptake, and several other biological functions. However, brain injury-induced energy and metabolic stressors, such as cerebral ischemia, increase AMPK phosphorylation. Phosphorylated AMPK contributes to excitotoxicity, oxidative, and metabolic problems. Furthermore, brain disease-induced release of zinc from synaptic vesicles contributes to neuronal damage via mechanisms including ROS production, apoptotic cell death, and DNA damage. For this reason, we hypothesized that regulating zinc accumulation and AMPK phosphorylation is critical for protection against global cerebral ischemia (GCI). Through virtual screening based on the structure of AMPK subunit alpha 2, we identified a novel compound, 2G11. In this study, we verified that 2G11 administration has neuroprotective effects via the blocking of zinc translocation and AMPK phosphorylation after GCI. As a result, we demonstrated that 2G11 protected hippocampal neurons against GCI and OGD/R-derived cellular damage. In conclusion, we propose that AMPK inhibition and zinc chelation by 2G11 may be a promising tool for preventing GCI-induced hippocampal neuronal death.

Two-Track Virtual Screening Approach to Identify the Dual Inhibitors of Wild Type and C481S Mutant of Bruton's Tyrosine Kinase

Bruton's tyrosine kinase (BTK) is responsible for the pathogenesis of various autoimmune diseases and chronic lymphocytic leukemia. However, the discovery of efficient medicines has seen limited success due to the constitutively active mutants that acquired the drug resistance. To disclose the dual inhibitors against the wild-type BTK and the problematic drug-resistant C481S mutant, a large chemical library was virtually screened with extensive molecular docking simulations using two target proteins. As a consequence of imposing the configurational restraint to make a hydrogen bond in the hinge region of BTK as well as modifying the ligand dehydration term in the scoring function, a total of 20 dual inhibitors were discovered with the range of the associated IC₅₀ values from 2.5 to 15 μM. All these dual inhibitors revealed the inhibitory activity against the C481S mutant to a comparable extent to that measured for the wild type. Among the new inhibitors, N-(3,5-dimethoxyphenyl)-6,7-dimethoxyquinazolin-4-amine (1) appeared to be most suitable as a starting point of the lead optimization due to the highest biochemical potency against the C481S mutant as well as the lowest molecular weight. To increase the potential of a drug candidate, 1 was modified into 6,7-dimethoxy-N-(pyridin-3-yl)quinazolin-4-amine (12) via chemical synthesis so as to possess better physicochemical properties without loss of the biochemical potency. 12 is suggested as a new effective molecular core from which numerous druggable dual inhibitors of the wild-type BTK and the C481S mutant would be derivatized.

Consensus Docking in Drug Discovery

Background:

Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets.

Methods:

In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose.

Results:

The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus.

Conclusion:

In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Molecular Architect: A User-Friendly Workflow for Virtual Screening

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

The role of human in the loop: lessons from D3R challenge 4

The rapid development of new machine learning techniques led to significant progress in the area of computer-aided drug design. However, despite the enormous predictive power of new methods, they lack explainability and are often used as black boxes. The most important decisions in drug discovery are still made by human experts who rely on intuitions and simplified representation of the field. We used D3R Grand Challenge 4 to model contributions of human experts during the prediction of the structure of protein-ligand complexes, and prediction of binding affinities for series of ligands in the context of absence or abundance of experimental data. We demonstrated that human decisions have a series of biases: a tendency to focus on easily identifiable protein-ligand interactions such as hydrogen bonds, and neglect for a more distributed and complex electrostatic interactions and solvation effects. While these biases still allow human experts to compete with blind algorithms in some areas, the underutilization of the information leads to significantly worse performance in data-rich tasks such as binding affinity prediction.

Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects

Peptidyl-prolyl cis-trans isomerase, NIMA-interacting 1 (PIN1) is a peptidyl-prolyl isomerase that binds phospho-Ser/Thr-Pro motifs in proteins and catalyzes the cis-trans isomerization of proline peptide bonds. PIN1 is overexpressed in several cancers including high-grade serous ovarian cancer. Since few therapies are effective against this cancer, PIN1 could be a therapeutic target but effective PIN1 inhibitors are lacking. To identify molecules with in vivo inhibitory effects on PIN1, we used consensus docking to model existing PIN1-ligand X-ray structures and to screen a chemical database for candidate inhibitors. Ten molecules were selected and tested in cellular assays, leading to the identification of VS10 that bound and inhibited PIN1. VS10 treatment reduced the viability of ovarian cancer cell lines by inducing proteasomal PIN1 degradation, without effects on PIN1 transcription, and also reduced the levels of downstream targets β-catenin, cyclin D1, and pSer473-Akt. VS10 is a selective PIN1 inhibitor that may offer new opportunities for treating PIN1-overexpressing tumors.

Identifying New AMP-Activated Protein Kinase Inhibitors That Protect against Ischemic Brain Injury

We recently reported that AMP-activated protein kinase (AMPK) contributes to zinc-induced neuronal death by inducing Bim, a pro-apoptotic Bcl-2 homology domain 3-only protein, in a liver kinase B1 (LKB1)-dependent manner. Current data suggest AMPK plays key roles in excitotoxicity and ischemic brain injury, with zinc neurotoxicity representing at least one mechanism of ischemic neuronal death. Inhibition of AMPK could be a viable therapeutic strategy to prevent ischemic brain injury following stroke. This prompted our search for novel inhibitors of AMPK activity and zinc-induced neuronal death using cultured mouse cortex and a rat model of brain injury after middle cerebral artery occlusion (MCAO). In structure-based virtual screening, 118 compounds were predicted to bind the active site of AMPK α2, and 40 showed in vitro AMPK α2 inhibitory activity comparable to compound C (a well-known, potent AMPK inhibitor). In mouse cortical neuronal cultures, 7 of 40 compound reduced zinc-induced neuronal death at levels comparable to compound C. Ultimately, only agents 2G11 and 1H10 significantly attenuated various types of neuronal death, including oxidative stress, excitotoxicity, and apoptosis. When administered as intracerebroventricular injections prior to permanent MCAO in rats, 2G11 and 1H10 reduced brain infarct volumes, whereas compound C did not. Therefore, these novel AMPK inhibitors could be drug development candidates to treat stroke.

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

Identification of N-(5-(phenoxymethyl)-1,3,4-thiadiazol-2-yl)acetamide derivatives as novel protein tyrosine phosphatase epsilon inhibitors exhibiting anti-osteoclastic activity

Cytosolic protein tyrosine phosphatase epsilon (cyt-PTPε) plays a central role in controlling differentiation and function of osteoclasts, whose overactivation causes osteoporosis. Based on our previous study reporting a number of cyt-PTPε inhibitory chemical compounds, we carried out a further and extended analysis of our compounds to examine their effects on cyt-PTPε-mediated dephosphorylation and on osteoclast organization and differentiation. Among five compounds showing target selectivity to cyt-PTPε over three other phosphatases in vitro, two compounds exhibited an inhibitory effect against the dephosphorylation of cellular Src protein, the cyt-PTPε substrate. Moreover, these two compounds caused destabilization of the podosome structure that is necessary for the bone-resorbing activity of osteoclasts, and also attenuated cellular differentiation of monocytes into osteoclasts, without affecting cell viability. Therefore, these findings not only verified anti-osteoclastic effects of our cyt-PTPε inhibitory compounds, but also showed that cyt-PTPε expressed in osteoclasts could be a putative therapeutic target worth considering.

Discovery of New SIRT2 Inhibitors by Utilizing a Consensus Docking/Scoring Strategy and Structure-Activity Relationship Analysis

SIRT2, which is a NAD+ (nicotinamide adenine dinucleotide) dependent deacetylase, has been demonstrated to play an important role in the occurrence and development of a variety of diseases such as cancer, ischemia-reperfusion, and neurodegenerative diseases. Small molecule inhibitors of SIRT2 are thought to be potential interfering agents for relevant diseases. Discovery of SIRT2 inhibitors has attracted much attention recently. In this investigation, we adopted a consensus docking/scoring strategy to screen for novel SIRT2 inhibitors. Structural optimization and structure-activity relationship (SAR) analysis were then carried out on highly potent compounds with new scaffolds, which led to the discovery of 2-((5-benzyl-5H-[1,2,4]triazino[5,6-b]indol-3-yl)thio)-N-(naphthalen-1-yl)acetamide (SR86). This compound showed good activity against SIRT2 with an IC₅₀ value of 1.3 μM. SR86 did not exhibit activity against SIRT1 and SIRT3, implying a good selectivity for SIRT2. In in vitro cellular assays, SR86 displayed very good antiviability activity against breast cancer cell line MCF-7. In Western blot assays, SR86 showed considerable activity in blocking the deacetylation of α-tubulin, which is a typical substrate of SIRT2. Collectively, because of the new scaffold structure and good selectivity of SR86, it could serve as a promising lead compound, hence deserving further studies.

Targeting of AMP-activated protein kinase: prospects for computer-aided drug design

INTRODUCTION

Dysregulation of energy homeostasis has been implicated in a number of human chronic diseases including diabetes, obesity, cancer, and inflammation. Given the functional attributes as a central regulator of energy homeostasis, AMP-activated protein kinase (AMPK) is emerging as a therapeutic target for these diseases, and lines of evidence have highlighted the need for rational and robust screening systems for identifying specific AMPK modulators with a therapeutic potential for preventing and/or curing these diseases. Areas covered: Here, the authors review the recent advances in the understanding of three-dimensional structures of AMPK in relationship with the regulatory mechanisms, potentials of AMPK as a therapeutic target in human chronic diseases, and prospects of computer-based drug design for AMPK. Expert opinion: Accumulating information of AMPK structure has provided us with deep insight into the molecular basis underlying the regulatory mechanisms, and further discloses several structural domains, which can be served for a target site for computer-based drug design. Molecular docking and simulations provides useful information about the binding sites between potent drugs and AMPK as well as a rational screening format to discover isoform-specific AMPK modulators. For these reasons, the authors suggest that computer-aided virtual screening methods hold promise as a rational approach for discovering more specific AMPK modulators.

Application of Fragment-Based de Novo Design to the Discovery of Selective Picomolar Inhibitors of Glycogen Synthase Kinase-3 Beta

A systematic fragment-based de novo design procedure was developed and applied to discover new potent and selective inhibitors of glycogen synthase kinase-3 beta (GSK3β). Candidate inhibitors were generated to simultaneously maximize the biochemical potency and the specificity for GSK3β through three design steps: identification of the optimal molecular fragments for the three sub-binding regions, design of proper linking moieties to connect the fragmental building blocks, and final scoring of the generated molecules. By virtue of modifying the ligand hydration free energy term in the scoring function using hybrid scaled particle theory and the extended solvent-contact model, we identified several GSK3β inhibitors with biochemical potencies ranging from low nanomolar to picomolar levels. Among them, the two most potent inhibitors (12 and 27) are anticipated to serve as promising starting points of drug discovery for various diseases caused by GSK3β because of the high specificity for the inhibition of GSK3β.

Virtual Screening with Docking Simulations and Biochemical Evaluation of VHY Phosphatase Inhibitors

Although VH1-related member Y (VHY) phosphatase is responsible for the pathogenesis of neuroinflammatory diseases, no small-molecule inhibitor of VHY has been reported so far. Here we first report eight VHY inhibitors identified from molecular docking-based virtual screening and subsequent enzyme inhibition assays. These inhibitors exhibit good biochemical potencies against VHY, with associated IC50 values ranging from 1 to 9 µM. Because all these inhibitors were also screened in silico for having desirable physicochemical properties as a drug candidate, they deserve further investigation by structure-activity relationship studies to develop new medicines for the treatment of neuroinflammatory diseases. The structural features of VHY-inhibitor interactions relevant to the micromolar-level inhibitory activity are addressed in detail.

Function-specific virtual screening for GPCR ligands using a combined scoring method

The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H₁ receptor (H₁R) antagonists/inverse agonists, 2) the selective structure-based identification of ß₂-adrenoceptor (ß₂R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H₁R and ß₂R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß₂R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß₂R and 73% of the H₁R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß₂R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

Ligand-protein docking is one of the most common techniques used in virtual screening campaigns. Despite the large number of docking software available, there is still the need of improving the efficacy of docking-based virtual screenings. To date, only very few studies evaluated the possibility of combining the results of different docking methods to achieve higher success rates in virtual screening studies (consensus docking). In order to better understand the range of applicability of this approach, we carried out an extensive enriched database analysis using the DUD dataset. The consensus docking protocol was then refined by applying modifications concerning the calculation of pose consensus and the combination of docking methods included in the procedure. The results obtained suggest that this approach performs as well as the best available methods found in literature, confirming the idea that this procedure can be profitably used for the identification of new hit compounds.

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

HIGHLIGHTS

Many CNS targets are being explored for multi-target drug design

New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds

QSAR, virtual screening and docking methods increase the potential of rational drug design

The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D₁-R/D₂-R/5-HT_2A-R/H₃-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.

Discovery of Low Micromolar Dual Inhibitors for Wild Type and L1196M Mutant of Anaplastic Lymphoma Kinase through Structure-Based Virtual Screening

Although anaplastic lymphoma kinase (ALK) is involved in a variety of malignant human cancers, the emergence of constitutively active mutants with drug resistance has rendered it difficult to identify the new medicines for ALK-dependent cancers. To find the common inhibitors of the wild type ALK and the most abundant drug-resistant mutant (L1196M), we performed molecular docking-based virtual screening of a large chemical library in parallel for the two target proteins. As a consequence of augmenting the accuracy of the docking simulation by implementing a sophisticated hydration free energy term in the scoring function, 12 common inhibitors are discovered with the inhibitory activities ranging from submicromolar to low micromolar levels. The results of the binding free energy decomposition indicate that the biochemical potency of ALK inhibitors can be optimized by reducing the dehydration cost for binding to the receptor protein as well as by strengthening the interactions with amino acid residues in the ATP-binding site. The newly identified ALK inhibitors are found to have a little higher inhibitory activity for the L1196M mutant than for the wild type due to the strengthening of the hydrogen bond interactions in the ATP-binding site. Of the 12 common inhibitors, 2-(5-methyl-benzooxazol-2-ylamino)-quinazolin-4-ol (3) is anticipated to serve as a new molecular scaffold to optimize the biochemical potency because it exhibits low micromolar inhibitory activity with respect to both the wild type and L1196M mutant in spite of the low molecular weight (292.3 amu).

Discovery of Dual Inhibitors for Wild Type and D816V Mutant of c-KIT Kinase through Virtual and Biochemical Screening of Natural Products

Although stem cell factor receptor (c-KIT) kinase is responsible for various malignant human cancers, the presence of constitutively active gain-of-function mutants has made it difficult to discover new anticancer agents using c-KIT as the target protein. To identify the common inhibitors of wild-type c-KIT and the most abundant gain-of-function mutant (D816V), the virtual screening of natural products was performed for the two target proteins in parallel with the scoring function improved by implementing a sophisticated solvation free energy term. As a result, four common inhibitors of natural origin are found with biochemical potencies ranging from low micromolar to submicromolar levels. The results of extensive docking simulations show that although the natural-product inhibitors establish weaker hydrophobic interactions with the D816V mutant than with the wild type, they exhibit a little higher inhibitory activity for the former than the latter by strengthening the hydrogen-bond interactions to a sufficient extent. Of the four natural-product inhibitors, (Z)-6-hydroxy-2-(4-methoxybenzylidene)benzofuran-3(2H)-one (3) is anticipated to serve as a new molecular core for the structure-activity relationship studies to optimize the biochemical potencies because it exhibits good inhibitory activity against both the wild type and D816V mutant despite its low molecular weight (268.3 amu).

AMP-activated protein kinase contributes to zinc-induced neuronal death via activation by LKB1 and induction of Bim in mouse cortical cultures

Background

We reported that zinc neurotoxicity, a key mechanism of ischemic neuronal death, was mediated by poly ADP-ribose polymerase (PARP) over-activation following NAD⁺/ATP depletion in cortical cultures. Because AMP-activated protein kinase (AMPK) can be activated by ATP depletion, and AMPK plays a key role in excitotoxicity and ischemic neuronal death, we examined whether AMPK could be involved in zinc neurotoxicity in mouse cortical neuronal cultures.

Results

Compound C, an AMPK inhibitor, significantly attenuated zinc-induced neuronal death. Activation of AMPK was detected beginning 2 h after a 10-min exposure of mouse cortical neurons to 300 μM zinc, although a significant change in AMP level was not detected until 4 h after zinc treatment. Thus, AMPK activation might not have been induced by an increase in intracellular AMP in zinc neurotoxicity. Furthermore, we observed that liver kinase B1 (LKB1) but not Ca²⁺/calmodulin-dependent protein kinase kinase β (CaMKKβ), was involved in AMPK activation. Although STO-609, a chemical inhibitor of CaMKKβ, significantly attenuated zinc neurotoxicity, zinc-induced AMPK activation was not affected, which suggested that CaMKKβ was not involved in AMPK activation. Knockdown of LKB1 by siRNA significantly reduced zinc neurotoxicity, as well as zinc-induced AMPK activation, which indicated a possible role for LKB1 as an upstream kinase for AMPK activation. In addition, mRNA and protein levels of Bim, a pro-apoptotic Bcl-2 family member, were noticeably increased by zinc in an AMPK-dependent manner. Finally, caspase-3 activation in zinc-induced neuronal death was mediated by LKB1 and AMPK activation.

Conclusions

The results suggested that AMPK mediated zinc-induced neuronal death via up-regulation of Bim and activation of caspase-3. Rapid activation of AMPK was detected after exposure of cortical neuronal cultures to zinc, which was induced by LKB1 activation but not increased intracellular AMP levels or CaMKKβ activation. Hence, blockade of AMPK in the brain may protect against zinc neurotoxicity, which is likely to occur after acute brain injury.

The Augmenting Effects of Desolvation and Conformational Energy Terms on the Predictions of Docking Programs against mPGES-1

In this study we introduce a rescoring method to improve the accuracy of docking programs against mPGES-1. The rescoring method developed is a result of extensive computational study in which different scoring functions and molecular descriptors were combined to develop consensus and rescoring methods. 127 mPGES-1 inhibitors were collected from literature and were segregated into training and external test sets. Docking of the 27 training set compounds was carried out using default settings in AutoDock Vina, AutoDock, DOCK6 and GOLD programs. The programs showed low to moderate correlation with the experimental activities. In order to introduce the contributions of desolvation penalty and conformation energy of the inhibitors various molecular descriptors were calculated. Later, rescoring method was developed as empirical sum of normalised values of docking scores, LogP and Nrotb. The results clearly indicated that LogP and Nrotb recuperate the predictions of these docking programs. Further the efficiency of the rescoring method was validated using 100 test set compounds. The accurate prediction of binding affinities for analogues of the same compounds is a major challenge for many of the existing docking programs; in the present study the high correlation obtained for experimental and predicted pIC₅₀ values for the test set compounds validates the efficiency of the scoring method.

Natural products as leads in schistosome drug discovery

Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ), the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs) as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS) strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.