SHAFTS: A Hybrid Approach for 3D Molecular Similarity Calculation. 2. Prospective Case Study in the Discovery of Diverse p90 Ribosomal S6 Protein Kinase 2 Inhibitors To Suppress Cell Migration

Synthesis, Nematicidal Evaluation, and the Structure-Activity Relationship Study of Aurone Derivatives

Plant-parasitic nematodes (PPNs) are one of the major threats to modern agriculture. Chemical nematicides are still required for the management of PPNs. Based on our previous work, the structure of aurone analogues was obtained using a hybrid 3D similarity calculation method (SHAFTS, SHApe-FeaTure Similarity). Thirty-seven compounds were synthesized. The nematicidal activity of target compounds against Meloidogyne incognita (root-knot nematode, M. incognita) was evaluated, and the structure-activity relationship of synthesized compounds was analyzed. The results showed that compound 6 and some of its derivatives exhibited impressive nematicidal activity. Among these compounds, compound 32 bearing 6-F showed the best in vitro and in vivo nematicidal activity. Its lethal concentration 50% after exposure to 72 h (LC_50/72 h) value was 1.75 mg/L, and the inhibition rate reached 97.93% in the sand at 40 mg/L. At the same time, compound 32 also exhibited excellent inhibition on egg hatching and moderate inhibition on the motility of Caenorhabditis elegans (C. elegans).

Inhibition of the dopamine transporter promotes lysosome biogenesis and ameliorates Alzheimer's disease-like symptoms in mice

INTRODUCTION

Lysosomes are degradative organelles that maintain cellular homeostasis and protein quality control. Transcription factor EB (TFEB)-mediated lysosome biogenesis enhances lysosome-dependent degradation and alleviates neurodegenerative diseases, but the mechanisms underlying TFEB regulation and modification are still poorly understood.

METHODS

By screening novel small-molecule compounds, we identified a group of lysosome-enhancing compounds (LYECs) that promote TFEB activation and lysosome biogenesis.

RESULTS

One of these compounds, LH2-051, significantly inhibited the function of the dopamine transporter (DAT) and subsequently promoted lysosome biogenesis. We uncovered cyclin-dependent kinase 9 (CDK9) as a novel regulator of DAT-mediated lysosome biogenesis and identified six novel CDK9-phosphorylated sites on TFEB. We observed that signal transduction by the DAT-CDK9-TFEB axis occurs on lysosomes. Finally, we found that LH2-051 enhanced the degradation of amyloid beta plaques and improved the memory of amyloid precursor protein (APP)/Presenilin 1 (PS1) mice.

DISCUSSION

We identified the DAT-CDK9-TFEB signaling axis as a novel regulator of lysosome biogenesis. Our study sheds light on the mechanisms of protein quality control under pathophysiological conditions.

FitDock: protein-ligand docking by template fitting

Protein-ligand docking is an essential method in computer-aided drug design and structural bioinformatics. It can be used to identify active compounds and reveal molecular mechanisms of biological processes. A successful docking usually requires thorough conformation sampling and scoring, which are computationally expensive and difficult. Recent studies demonstrated that it can be beneficial to docking with the guidance of existing similar co-crystal structures. In this work, we developed a protein-ligand docking method, named FitDock, which fits initial conformation to the given template using a hierarchical multi-feature alignment approach, subsequently explores the possible conformations and finally outputs refined docking poses. In our comprehensive benchmark tests, FitDock showed 40%-60% improvement in terms of docking success rate and an order of magnitude faster over popular docking methods, if template structures exist (> 0.5 ligand similarity). FitDock has been implemented in a user-friendly program, which could serve as a convenient tool for drug design and molecular mechanism exploration. It is now freely available for academic users at http://cao.labshare.cn/fitdock/.

Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.

A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening

Three-dimensional (3D) molecular similarity, one major ligand-based virtual screening (VS) method, has been widely used in the drug discovery process. A variety of 3D molecular similarity tools have been developed in recent decades. In this study, we assessed a panel of 15 3D molecular similarity programs against the DUD-E and LIT-PCBA datasets, including commercial ROCS and Phase, in terms of screening power and scaffold-hopping power. The results revealed that (1) SHAFTS, LS-align, Phase Shape_Pharm and LIGSIFT showed the best VS capability in terms of screening power. Some 3D similarity tools available to academia can yield relatively better VS performance than commercial ROCS and Phase software. (2) Current 3D similarity VS tools exhibit a considerable ability to capture actives with new chemotypes in terms of scaffold hopping. (3) Multiple conformers relative to single conformations will generally improve VS performance for most 3D similarity tools, with marginal improvement observed in area under the receiving operator characteristic curve values, enrichment factor in the top 1% and hit rate in the top 1% values showed larger improvement. Moreover, redundancy and complementarity analyses of hit lists from different query seeds and different 3D similarity VS tools showed that the combination of different query seeds and/or different 3D similarity tools in VS campaigns retrieved more (and more diverse) active molecules. These findings provide useful information for guiding choices of the optimal 3D molecular similarity tools for VS practices and designing possible combination strategies to discover more diverse active compounds.

Virtual Screening in the Cloud: How Big Is Big Enough?

Virtual screening is a standard tool in Computer-Assisted Drug Design (CADD). Early in a project, it is typical to use ligand-based similarity search methods to find suitable hit molecules. However, the number of compounds which can be screened and the time required are usually limited by computational resources. We describe here a high-throughput virtual screening project using 3D similarity (FastROCS) and automated evaluation workflows on Orion, a cloud computing platform. Cloud resources make this approach fully scalable and flexible, allowing the generation and search of billions of virtual molecules, and give access to an explicit 3D virtual chemistry space not available before. We discuss the impact of the size of the search space with respect to finding novel chemical hits and the size of the required hit list, as well as computational and economical aspects of resource scaling.

Identification of New Inhibitors of Mutant Isocitrate Dehydrogenase 2 through Molecular Similarity-based Virtual Screening

Background:

Isocitrate dehydrogenase 2 (IDH2) is an enzyme catalyzing the oxidative decarboxylation of isocitrate to α-ketoglutarate (α-KG) in the tricarboxylic acid (TCA). Evidences suggest that the specific mutations in IDH2 are critical to the growth and reproduction of severe tumors especially leukemia and glioblastoma. It is found that the inhibitors of mutant IDH2 are promising anti-tumor therapeutics.

Methods:

A virtual screening strategy combining molecular similarity search and molecular docking was performed in the binding site of AGI-6780. YL-16, YL-17 and YL-18 were identified as novel mutant IDH2 inhibitors for the reduction of (D)-2-hydroxyglutarate in cellular evaluation. In addition, all the three compounds showed inhibition against IDH2-R172K mutated HEK-293T cells, while weak inhibition against wide-type IDH2 (WT-IDH2) HEK-293T cells.

Results:

Significantly, YL-17 showed 84.55% inhibitory activity against IDH2-R172K at 1 µM and weak cytotoxicity to wide-type IDH2 at 50 µM.

Conclusion:

YL-17 was highlighted as a new mutant IDH2 inhibitor that could be further developed for therapeutic applications.

Discovery of SCY45, a Natural Small-Molecule MDM2-p53 Interaction Inhibitor

The disruption of the MDM2-p53 interaction has been regarded as an attractive strategy for anticancer drug discovery. Here, the natural small-molecule SCY45 was identified as a potent MDM2-p53 interaction inhibitor based on fluorescence polarization and molecular modeling. SCY45 inhibited the MDM2-p53 interaction with an IC₅₀ value of 4.93±0.08 μm. The structural modeling results showed that SCY45 not only had high structural similarity with nutlin-3a, a well-reported MDM2-P53 interaction inhibitor, but also bound to the p53 binding pocket of MDM2 with a binding mode similar to that of nutlin-3a. Moreover, SCY45 reduced the cell viability in cancer cells with MDM2 gene amplification. SCY45 showed the highest inhibition for SJSA-1 cells, which exhibit excessive MDM2 gene amplification, with an IC₅₀ value of 7.54±0.29 μm, whereas SCY45 showed a weaker inhibition for 22Rv1 cells and A549 cells, which have a single copy of the MDM2 gene, with IC₅₀ values of 18.47±0.75 μm and 31.62±1.96 μm, respectively.

Collaborative drug discovery and the Tres Cantos Antimalarial Set (TCAMS)

Malaria affects a population of over 200 million people worldwide. New drugs are needed because of widespread resistance, and the hunt for such drugs involves a coordinated research effort from the scientific community. The release of the Tres Cantos Antimalarial Set (TCAMS) in 2010 represented a landmark in the field of collaborative drug discovery for malaria. This set of >13 000 molecules with confirmed activity against several strains of Plasmodium falciparum was publicly released with the goal of fostering additional research beyond the GlaxoSmithKline (GSK) network of collaborators. Here, we examine the outcomes realized from TCAMS over the past 8 years and whether the expectations surrounding this initiative have become a reality.

Discovery of a Natural-Product-Derived Preclinical Candidate for Once-Weekly Treatment of Type 2 Diabetes

Poor medication adherence is one of the leading causes of suboptimal glycaemic control in approximately half of the patients with type 2 diabetes mellitus (T2DM). Long-acting antidiabetic drugs are clinically needed for improving patients' compliance. Dipeptidyl peptidase-4 (DPP-4) inhibitors play an increasingly important role in the treatment of T2DM because of their favorable properties of weight neutrality and hypoglycemia avoidance. Herein, we report the successful discovery and scale-up synthesis of compound 5, a structurally novel, potent, and long-acting DPP-4 inhibitor for the once-weekly treatment of T2DM. Inhibitor 5 has fast-associating and slow-dissociating binding kinetics profiles as well as slow clearance rate and long terminal half-life pharmacokinetic properties. A single-dose oral administration of 5 (3 mg/kg) inhibited >80% of DPP-4 activity for more than 7 days in diabetic mice. The long-term antidiabetic efficacies of 5 (10 mg/kg, qw) were better than those of the once-weekly trelagliptin and omarigliptin, especially in decreasing the hemoglobin A1c level.

Identifying a novel anticancer agent with microtubule-stabilizing effects through computational cell-based bioactivity prediction models and bioassays

G03 is a novel anticancer agent with unusual microtubule-stabilizing effects.

3(2H)-pyridazinone derivatives: a new scaffold for novel plant activators

Due to the emergence of drug resistance, pesticide residue and environmental contamination, it is important to develop novel eco-friendly strategies to protect plants.

A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data

Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Computer-Aided Drug Design in Epigenetics

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor

D3R 2016 Grand Challenge 2 focused on predictions of binding modes and affinities for 102 compounds against the farnesoid X receptor (FXR). In this challenge, two distinct methods, a docking-based method and a template-based method, were employed by our team for the binding mode prediction. For the new template-based method, 3D ligand similarities were calculated for each query compound against the ligands in the co-crystal structures of FXR available in Protein Data Bank. The binding mode was predicted based on the co-crystal protein structure containing the ligand with the best ligand similarity score against the query compound. For the FXR dataset, the template-based method achieved a better performance than the docking-based method on the binding mode prediction. For the binding affinity prediction, an in-house knowledge-based scoring function ITScore2 and MM/PBSA approach were employed. Good performance was achieved for MM/PBSA, whereas the performance of ITScore2 was sensitive to ligand composition, e.g. the percentage of carbon atoms in the compounds. The sensitivity to ligand composition could be a clue for the further improvement of our knowledge-based scoring function.

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

Virtual screening applications in short-chain dehydrogenase/reductase research

Several members of the short-chain dehydrogenase/reductase (SDR) enzyme family play fundamental roles in adrenal and gonadal steroidogenesis as well as in the metabolism of steroids, oxysterols, bile acids, and retinoids in peripheral tissues, thereby controlling the local activation of their cognate receptors. Some of these SDRs are considered as promising therapeutic targets, for example to treat estrogen-/androgen-dependent and corticosteroid-related diseases, whereas others are considered as anti-targets as their inhibition may lead to disturbances of endocrine functions, thereby contributing to the development and progression of diseases. Nevertheless, the physiological functions of about half of all SDR members are still unknown. In this respect, in silico tools are highly valuable in drug discovery for lead molecule identification, in toxicology screenings to facilitate the identification of hazardous chemicals, and in fundamental research for substrate identification and enzyme characterization. Regarding SDRs, computational methods have been employed for a variety of applications including drug discovery, enzyme characterization and substrate identification, as well as identification of potential endocrine disrupting chemicals (EDC). This review provides an overview of the efforts undertaken in the field of virtual screening supported identification of bioactive molecules in SDR research. In addition, it presents an outlook and addresses the opportunities and limitations of computational modeling and in vitro validation methods.

Structural evidence of quercetin multi-target bioactivity: A reverse virtual screening strategy

The ubiquitous flavonoid quercetin is broadly recognized for showing diverse biological and health-promoting effects, such as anti-cancer, anti-inflammatory and cytoprotective activities. The therapeutic potential of quercetin and similar compounds for preventing such diverse oxidative stress-related pathologies has been generally attributed to their direct antioxidant properties. Nevertheless, accumulated evidence indicates that quercetin is also able to interact with multiple cellular targets influencing the activity of diverse signaling pathways. Even though there are a number of well-established protein targets such as phosphatidylinositol 3 kinase and xanthine oxidase, there remains a lack of a comprehensive knowledge of the potential mechanisms of action of quercetin and its target space. In the present work we adopted a reverse screening strategy based on ligand similarity (SHAFTS) and target structure (idTarget, LIBRA) resulting in a set of predicted protein target candidates. Furthermore, using this method we corroborated a broad array of previously experimentally tested candidates among the predicted targets, supporting the suitability of this screening approach. Notably, all of the predicted target candidates belonged to two main protein families, protein kinases and poly [ADP-ribose] polymerases. They also included key proteins involved at different points within the same signaling pathways or within interconnected signaling pathways, supporting a pleiotropic, multilevel and potentially synergistic mechanism of action of quercetin. In this context we highlight the value of quercetin's broad target profile for its therapeutic potential in diseases like inflammation, neurodegeneration and cancer.

Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.

Discovery and Rational Design of Natural-Product-Derived 2-Phenyl-3,4-dihydro-2H-benzo[f]chromen-3-amine Analogs as Novel and Potent Dipeptidyl Peptidase 4 (DPP-4) Inhibitors for the Treatment of Type 2 Diabetes

Starting from the lead isodaphnetin, a natural product inhibitor of DPP-4 discovered through a target fishing docking based approach, a series of novel 2-phenyl-3,4-dihydro-2H-benzo[f]chromen-3-amine derivatives as potent DPP-4 inhibitors are rationally designed utilizing highly efficient 3D molecular similarity based scaffold hopping as well as electrostatic complementary methods. Those ingenious drug design strategies bring us approximate 7400-fold boost in potency. Compounds 22a and 24a are the most potent ones (IC50 ≈ 2.0 nM) with good pharmacokinetic profiles. Compound 22a demonstrated stable pharmacological effect. A 3 mg/kg oral dose provided >80% inhibition of DPP-4 activity within 24 h, which is comparable to the performance of the long-acting control omarigliptin. Moreover, the efficacy of 22a in improving the glucose tolerance is also comparable with omarigliptin. In this study, not only promising DPP-4 inhibitors as long acting antidiabetic that are clinically on demand are identified, but the target fish docking and medicinal chemistry strategies were successfully implemented.

Isochamaejasmin induces apoptosis in leukemia cells through inhibiting Bcl-2 family proteins

The biflavonoid isochamaejasmin is mainly distributed in the root of Stellera chamaejasme L. (Thymelaeaceae) that is used in traditional Chinese medicine (TCM) to treat tumors, tuberculosis, and psoriasis. Herein, isochamaejasmin was found to show similar bioactivity against Bcl-2 family proteins to the reference Bcl-2 ligand (-)-gossypol through 3D similarity search. It selectively bound to Bcl-xl and Mcl-1 with Ki values being 1.93 ± 0.13 μmol·L(-1) and 9.98 ± 0.21 μmol·L(-1), respectively. In addition, isochamaejasmin showed slight growth inhibitory activity against HL-60 with IC50 value being 50.40 ± 1.21 μmol·L(-1) and moderate growth inhibitory activity against K562 cells with IC50 value being 24.51 ± 1.62 μmol·L(-1). Furthermore, isochamaejasmin induced apoptosis of K562 cells by increasing the intracellular expression levels of proteins of the cleavage of caspase-9, caspase-3, and PARP which involved in the Bcl-2-induced apoptosis pathway. These results indicated that isochamaejasmin induces apoptosis in leukemia cells by inhibiting the activity of Bcl-2 family proteins, providing evidence for further studying the underlying anti-cancer mechanism of S. chamaejasme L.

Identification of novel small-molecule inhibitors targeting menin–MLL interaction, repurposing the antidiarrheal loperamide

Scaffold hopping combines with biochemical studies and medicinal chemistry optimizations, leading to potent inhibitors of the menin–MLL interaction.

Isoindole-1,3-dione derivatives as RSK2 inhibitors: synthesis, molecular docking simulation and SAR analysis

The present study reports a series of novel potent RSK2 inhibitors obtained from structure modifications of a virtual screening hit.

The discovery of new plant activators and scaffolds with potential induced systemic resistance: from jasmonic acid to pyrrolidone

A series of novel plant activators possessing a pyrrolidone scaffold was developed with the help of SHAFTS.

HybridDock: A Hybrid Protein-Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches

Structure-based molecular docking and ligand-based similarity search are two commonly used computational methods in computer-aided drug design. Structure-based docking tries to utilize the structural information on a drug target like protein, and ligand-based screening takes advantage of the information on known ligands for a target. Given their different advantages, it would be desirable to use both protein- and ligand-based approaches in drug discovery when information for both the protein and known ligands is available. Here, we have presented a general hybrid docking protocol, referred to as HybridDock, to utilize both the protein structures and known ligands by combining the molecular docking program MDock and the ligand-based similarity search method SHAFTS, and evaluated our hybrid docking protocol on the CSAR 2013 and 2014 exercises. The results showed that overall our hybrid docking protocol significantly improved the performance in both binding affinity and binding mode predictions, compared to the sole MDock program. The efficacy of the hybrid docking protocol was further confirmed using the combination of DOCK and SHAFTS, suggesting an alternative docking approach for modern drug design/discovery.

Bioactive cembranoids from the South China Sea soft coral Sarcophyton elegans

Four new cembranoids, sarcophelegans A–D (1–4) and six known analogues (5–10) were isolated from the South China Sea soft coral Sarcophyton elegans. Their structures were elucidated through detailed spectroscopic analysis, and the absolute configuration of 1 was confirmed by single-crystal X-ray diffraction. The antimigratory potential of compounds 1–10 were evaluated and compounds 2 and 6 were found to inhibit human breast tumor MDA-MB-231 cell migration at 10 μM.

Myricetin inhibits proliferation and induces apoptosis and cell cycle arrest in gastric cancer cells

Myricetin is a flavonoid that is abundant in fruits and vegetables and has protective effects against cancer and diabetes. However, the mechanism of action of myricetin against gastric cancer (GC) is not fully understood. We researched myricetin on the proliferation, apoptosis, and cell cycle in GC HGC-27 and SGC7901 cells, to explore the underlying mechanism of action. Cell Counting Kit (CCK)-8 assay, Western blotting, cell cycle analysis, and apoptosis assay were used to evaluate the effects of myricetin on cell proliferation, apoptosis, and the cell cycle. To analyze the binding properties of ribosomal S6 kinase 2 (RSK2) with myricetin, surface plasmon resonance (SPR) analysis was performed. CCK8 assay showed that myricetin inhibited GC cell proliferation. Flow cytometry analysis showed that myricetin induces apoptosis and cell cycle arrest in GC cells. Western blotting indicated that myricetin influenced apoptosis and cell cycle arrest of GC cells by regulating related proteins. SPR analysis showed strong binding affinity of RSK2 and myricetin. Myricetin bound to RSK2, leading to increased expression of Mad1, and contributed to inhibition of HGC-27 and SGC7901 cell proliferation. Our results suggest the therapeutic potential of myricetin in GC.

Systematic Analysis of the Multiple Bioactivities of Green Tea through a Network Pharmacology Approach

During the past decades, a number of studies have demonstrated multiple beneficial health effects of green tea. Polyphenolics are the most biologically active components of green tea. Many targets can be targeted or affected by polyphenolics. In this study, we excavated all of the targets of green tea polyphenolics (GTPs) though literature mining and target calculation and analyzed the multiple pharmacology actions of green tea comprehensively through a network pharmacology approach. In the end, a total of 200 Homo sapiens targets were identified for fifteen GTPs. These targets were classified into six groups according to their related disease, which included cancer, diabetes, neurodegenerative disease, cardiovascular disease, muscular disease, and inflammation. Moreover, these targets mapped into 143 KEGG pathways, 26 of which were more enriched, as determined though pathway enrichment analysis and target-pathway network analysis. Among the identified pathways, 20 pathways were selected for analyzing the mechanisms of green tea in these diseases. Overall, this study systematically illustrated the mechanisms of the pleiotropic activity of green tea by analyzing the corresponding “drug-target-pathway-disease” interaction network.

Myricetin exerts anti-proliferative, anti-invasive, and pro-apoptotic effects on esophageal carcinoma EC9706 and KYSE30 cells via RSK2

Myricetin, a common dietary flavonoid, is widely distributed in fruits and vegetables and is used as a health food supplement based on its anti-tumor properties. However, the effect and mechanisms of myricetin in esophageal carcinoma are not fully understood. Here, we demonstrated the effect of myricetin on the proliferation, apoptosis, and invasion of the esophageal carcinoma cell lines EC9706 and KYSE30 and explored the underlying mechanism and target protein(s) of myricetin. CCK-8 assay, transwell invasion assay, wound-healing assay, cell cycle analysis, and apoptosis assay were used to evaluate the effects of myricetin on cell proliferation, invasion, and apoptosis. Nude mouse tumor xenograft model was built to understand the interaction between myricetin and NTD RSK2. Pull-down assay was used to verify molecular mechanism. Myricetin inhibited proliferation and invasion and induced apoptosis of EC9706 and KYSE30 cells. Moreover, myricetin was shown to bind RSK2 through the NH2-terminal kinase domain. Finally, myricetin inhibited EC9706 and KYSE30 cell proliferation through Mad1 and induced cell apoptosis via Bad. Myricetin inhibits the proliferation and invasion and induces apoptosis in EC9706 and KYSE30 cells via RSK2. Myricetin exerts anti-proliferative, anti-invasive, and pro-apoptotic effects on esophageal carcinoma EC9706 and KYSE30 cells via RSK2. Our results provide novel insight into myricetin as a potential agent for the prevention and treatment of esophageal carcinoma.

iDrug: a web-accessible and interactive drug discovery and design platform

BACKGROUND

The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed.

RESULTS

We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work.

CONCLUSIONS

iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug.

Antiproliferative activity and therapeutic implications of potassium tris(4-methyl-1-pyrazolyl) borohydride in hepatocellular carcinoma

The study of iron chelators as cancer chemotherapeutic agents is still in its infancy. Accordingly, there is a need to optimize new chelating molecules for iron chelation therapy and cancer treatment. Previous studies have demonstrated that the ligand tris(1-pyrazolyl) borohydride and its derivates were able to chelate ferrous iron, but very little research focused on their biological properties and applications in cancer treatment. So, in this study, several boron-pyrazole derivatives were chosen for the examination of their effects on the proliferation of human hepatocellular carcinoma (HCC) cell lines. The results suggested that potassium tris(4-methyl-1-pyrazolyl) borohydride (KTp(4-Me)) exhibited the most potent anti-tumor activities among the candidates. Hence, the antiproliferative activity and the iron chelating capacity of the iron chelator KTp(4-Me) in HCC cell lines HepG2 and Hep3B were characterized. KTp(4-Me) could disrupt cell iron uptake and affect signaling pathways of iron regulation in HCC cell lines and induced the expression of TfR1 and HIF-1α in a concentration-dependent manner, which was a typical cell response to iron deficiency. Moreover, KTp(4-Me) arrested cell cycle in S phase and induced cell apoptosis in both Hep3B and HepG2 cells. Overall, our results provide a promising starting point and the possibility of the future development and applications of KTp(4-Me) in HCC therapy.

Computational methods for drug design and discovery: focus on China

In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field.