基于药效团模型虚拟筛选活络丸中治疗骨关节炎的COX-2抑制成分

目的 采用药效团模型-分子对接-结合自由计算的分层虚拟筛选策略结合酶活性抑制实验,挖掘活络丸中对环氧合酶2(cyclooxygenase-2, COX-2)有抑制作用的成分。方法 使用Schrödinger 2020-4软件中PHASE模块进行COX-2小分子抑制剂药效团模型的构建,应用筛选出来的最佳药效团模型对活络丸中药化合物库进行虚拟筛选,对筛选到符合药效团模型的成分进行基于靶点结构的分子对接、结合自由能计算,选择潜在抑制成分进行酶活性测定实验,并对活性较好的小分子进行药代动力学、毒理学性质预测。结果 构建的最佳药效团模型具有两个氢键受体和两个芳环中心;体外酶活性抑制实验结果显示,筛选出的11个潜在靶向抑制成分均对COX-2具有不同程度的抑制作用,其中抑制活性较强的成分为表儿茶素IC₅₀为(0.93±0.15)μmol·L^-1、木犀草素IC₅₀为(1.96±0.19)μmol·L^-1及槲皮素IC₅₀为(2.09±0.28)μmol·L^-1,ADMET计算发现木犀草素、表儿茶素、槲皮素成药性较好。结论 采用药效团模型、分子对接、结合自由能计算及体外酶活性抑制实验,挖掘活络丸中对COX-2有抑制作用的成分,为骨关节炎中药来源的单体成分的现代化研究提供线索。     

Pharmacophore modeling technique applied for the discovery of proteasome inhibitors

Introduction: The 26S proteasome has many important roles in the biological functions of the cells, and proteasome inhibitors have multiple and complex activities on cells. These compounds can be natural or synthesized. Most synthetic derivatives have been rationally designed, synthesized and optimized to obtain the best selectivity and increase the activity. The design of chemical entities with desired molecular identification, which plays an important role in biological systems, is provided by pharmacophore modeling. Indeed, pharmacophore models can be established either in a ligand-based manner or in a receptor-based manner.

Areas covered: The authors discuss the application of pharmacophore modeling techniques to proteasome inhibitors development. Furthermore, the article reviews the classification of the currently discovered proteasome inhibitors where the principal mechanism of action and clinical application are represented.

Expert opinion: In the era of new drug development, database of compounds should be thoroughly evaluated with a combination of methods that consider both pharmacophore- and ligand-based virtual screening. The concept of pharmacophore helps to discover new active compounds and to evaluate their activity. The nature of proteasome inhibitor pharmacophore affects the secondary active-site specificity; indeed, increasing specificity decreases the cytotoxicity of the proteasome inhibitors. It is hypothesized that the balanced simultaneous modulation of a few druggable targets may have superior efficacy and fewer side effects than single-target or combination therapies for the treatment of human cancers. The discovery of new compounds should aim to find more active compounds that improve the compliance of patients.     

Tubulin Inhibitor Identification by Bioactive Conformation Alignment Pharmacophore‐Guided Virtual Screening

Microtubules are important cellular component that are critical for proper cellular function. Microtubules are synthesized by polymerization of αβ tubulin heterodimers called protofilaments. Microtubule dynamics facilitate proper cell division during mitosis. Disruption of microtubule dynamics by small‐molecule agents inhibits mitosis, resulting in apoptotic cell death and preventing cell cycle progression. To identify a novel small molecule that binds the αβ tubulin interface to affect microtubule dynamics, we developed a bioactive conformation alignment pharmacophore (BCAP) model to screen tubulin inhibitors from a huge database. The application of BCAP model generated based on the known αβ‐tubulin interface binders enabled us to identify several small‐molecules that cause apoptosis in human promyelocytic leukemia (HL‐60) cells. Virtual screening combined with an in vitro assay yielded 15 cytotoxic molecules. In particular, ethyl 2‐(4‐(5‐methyl‐3‐nitro‐1H‐pyrazol‐1‐yl)butanamido)‐4‐phenylthiophene‐3‐carboxylate ( H05 ) inhibited tubulin polymerization with an IC₅₀ of 17.6 μm concentration. The virtual screening results suggest that the application of an unbiased BCAP pharmacophore greatly eliminates unlikely compounds from a huge database and maximizes screening success. From the limited compounds tested in the tubulin polymerization inhibitor (TPI) assay, compound H05 was discovered as a tubulin inhibitor. This compound requires further structure activity optimization to identify additional potent inhibitors from the same class of molecules.     

Receptor-Based Discovery of a Plasmalemmal Monoamine Transporter Inhibitor via High-Throughput Docking and Pharmacophore Modeling

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Pharmacophore-based virtual screening versus docking-based virtual screening： a benchmark comparison against eight targets

Aim:

This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods.

Methods:

All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors α (ERα), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS.

Results:

Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.

Conclusion:

The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.     

A Specific Pharmacophore Model of Aurora B Kinase Inhibitors and Virtual Screening Studies Based on it

In this study, 3D‐pharmacophore models of Aurora B kinase inhibitors have been developed by using HipHop and HypoGen modules in Catalyst software package. The best pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9911), consists of one hydrogen‐bond acceptor, one hydrogen‐bond donor, one hydrophobic aliphatic moiety and one ring aromatic feature. Hypo1 was validated by test set and cross‐validation methods. And the specificity of Hypo1 to Aurora B inhibitors was examined with the use of selective inhibitors against Aurora B and its paralogue Aurora A. The results clearly indicate that Hypo1 can differentiate selective inhibitors of Aurora B from those of Aurora A, and the ring aromatic feature likely plays some important roles for the specificity of Hypo1. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD) for identifying new inhibitors of Aurora B. The hit compounds were subsequently subjected to filtering by Lipinski’s rule of five and docking studies to refine the retrieved hits, and some compounds selected from the top ranked hits have been suggested for further experimental assay studies.     

Pharmacophore Identification and Validation Study of CK2 Inhibitors Using CoMFA/CoMSIA

Protein kinase CK2, also known as casein kinase‐2, has been found to be involved in cell growth, proliferation and suppression of apoptosis, which is related to human cancers. The series of compounds were identified as casein kinase‐2 inhibitors and their inhibitory activities are a function of a variation of their structures. The current study deals with the pharmacophore identification and, accordingly, the three‐dimensional quantitative structure–activity relationship model development using Pharmacophore Alignment and Scoring Engine. Several hypotheses were developed for the molecular alignments. On the basis of statistical values, the best‐fitted model was identified and the same alignment was used for 3D‐QSAR using comparative molecular field analysis/comparative molecular similarity index analysis. Both the CoMFA (R ²_CV = 0.58, R ² = 0.82 and r ²_pred = 0.62) and the comparative molecular similarity index analysis (R ²_CV = 0.74, R ² = 0.98 and r ²_pred = 0.81) gave reasonable results. Besides pharmacophore‐based alignment, the maximum common substructure‐based alignment was also used for the comparative molecular field analysis and comparative molecular similarity index analysis. The pharmacophore‐based alignment was more prominent and it has provided important information for the modelling of potent inhibitors. The overall study implies that a highly positive and bulky group with H‐bond donating property is desirable around the nitrogen atom adjacent to the pyrrolidine ring.     

Identification of promising DNA GyrB inhibitors for Tuberculosis using pharmacophore‐based virtual screening,molecular docking and molecular dynamics studies

In this study, we searched for potential DNA GyrB inhibitors using pharmacophore‐based virtual screening followed by molecular docking and molecular dynamics simulation approaches. For this purpose, a set of 248 DNA GyrB inhibitors was collected from the literature and a well‐validated pharmacophore model was generated. The best pharmacophore model explained that two each of hydrogen bond acceptors and hydrophobicity regions were critical for inhibition of DNA GyrB. Good statistical results of the pharmacophore model indicated that the model was robust in nature. Virtual screening of molecular databases revealed three molecules as potential antimycobacterial agents. The final screened promising compounds were evaluated in molecular docking and molecular dynamics simulation studies. In the molecular dynamics studies, RMSD and RMSF values undoubtedly explained that the screened compounds formed stable complexes with DNA GyrB. Therefore, it can be concluded that the compounds identified may have potential for the treatment of TB.     

基于药效团和分子对接的黄芪甲苷改善HepG2细胞胰岛素抵抗作用及机制研究

目的观察黄芪甲苷(astragalosideIV,ASTIV)改善人肝癌HepG2细胞胰岛素抵抗作用,基于药效团模型相互匹配和分子对接预测和验证AST IV可能作用靶点,探讨AST IV改善胰岛素抵抗机制。方法采用高浓度胰岛素诱导HepG2细胞制备胰岛素抵抗模型,ASTIV干预后,检测细胞葡萄糖消耗量,基于药效团模型相互匹配和分子对接预测ASTIV可能作用靶点,Western blotting法检测通路相关蛋白表达。结果 AST IV干预能显著增加胰岛素抵抗的HepG2细胞葡萄糖消耗量,且效应与盐酸吡格列酮相当;基于药效团模型相互匹配和分子对接预测AST IV作用靶点与酪氨酸磷酸酶1B(PTP1B)相关;Western blotting结果显示,胰岛素抵抗的HepG2细胞PTP1B蛋白表达水平显著升高,而胰岛素信号通路关键蛋白磷酸化的胰岛素受体(p-IR)和磷酸化的胰岛素受体底物1(p-IRS-1)表达水平显著降低;ASTIV的干预能显著降低PTP1B蛋白表达水平,升高p-IR和p-IRS-1蛋白表达水平。结论 ASTIV能显著改善高浓度胰岛素诱导的HepG2细胞的胰岛素抵抗,其作用机制与抑制PTP1B激活胰岛素信号通路有关。