野菊花中黄酮类成分抗炎活性的虚拟筛选

目的 前期研究及文献报道均表明野菊花黄酮类成分有明显抗炎活性,但其药效物质基础和作用机制尚未明确。该研究应用分子对接技术虚拟筛选野菊花黄酮抗炎活性的药效物质。方法 搜集现已分离鉴定的34个野菊花黄酮类化合物组成配体数据库,选择IκB激酶β（IKK-β）、P38、环氧合酶 2 （COX-2）、肿瘤坏死因子（TNF）等4个与抗炎活性密切相关的靶点组成受体数据库,应用Discovery Studio 3.0 (DS3.0)软件进行分子对接。结果 通过分子对接虚拟筛选,筛选出打分总分高于阈值的黄酮类化合物共11个。结论 对比分析了原配体与野菊花黄酮作用于各靶点的主要活性位点,初步推断了野菊花黄酮抗炎活性的作用机制,为研发抗炎制剂类药物提供了一定的参考。     

不可逆性酪氨酸激酶抑制剂的研究进展

酪氨酸激酶的过度表达和过度激活在许多肿瘤的发生和发展中具有重要意义,因此,多种酪氨酸激酶成为抗肿瘤药物的靶点。目前已经上市的小分子酪氨酸激酶抑制剂多属于可逆性抑制剂,这些药物具有选择性差、药效不够强烈和持久以及易引发耐药性等缺点。近些年,不可逆性酪氨酸激酶抑制剂的研究正方兴未艾。这一类药物分子以不可逆的共价键与酪氨酸激酶上ATP结合域进行结合,从而使该靶点永久性失活。由于其独特的作用机制,不可逆性酪氨酸激酶抑制剂可以有效地解决可逆性酪氨酸激酶抑制剂的几个缺点。目前,已经有一批不可逆性酪氨酸激酶抑制剂进入市场或临床研究阶段。该篇综述是对不可逆性酪氨酸激酶抑制剂的结构、药理和药化特征及其研究进展等进行总结和阐述。     

开发中的多靶点酪氨酸激酶抑制剂

多靶点酪氨酸激酶抑制剂是目前抗肿瘤药物研发的热点.相对于单靶点酪氨酸激酶抑制剂,其在疗效及患者耐受性方面都有很大的优势.本文综述蛋白酪氨酸激酶及其与肿瘤的关系,并简要介绍几种开发中的多靶点酪氨酸激酶抑制剂.     

苦参黄酮类化合物中GLUT4转运蛋白激动剂的虚拟筛选

目的 采用分子对接技术虚拟筛选苦参黄酮类化合物中葡萄糖转运蛋白4（GLUT4）转运蛋白激动剂。方法 搜集现已分离鉴定的128个苦参黄酮类化合物组成配体数据库,检索RCSB PDB数据库中葡萄糖转运蛋白4蛋白晶体结构并确定本研究用优势构象,以蛋白优势结构对应DrugBank中已上市的小分子药物平均打分为阈值,应用Discovery Studio 2.5（DS2.5）软件的CDOCKER模块进行分子对接。结果 虚拟筛选出打分高于阈值且排名前10%的化合物共12个并归纳了苦参黄酮作用靶点的主要活性位点,初步揭示了苦参黄酮抗糖尿病的作用机制。结论 基于分子对接的虚拟筛选可推断苦参黄酮化合物中促进GLUT4转运蛋白激动的有效成分,为进一步构效关系研究及研发基于新靶点的抗糖尿病类药物提供了一定的参考。     

选择性的激酶ATP竞争性抑制剂设计研究进展

激酶在细胞的生命过程中起着至关重要的作用，其功能异常会导致包括肿瘤在内的许多重大疾病的发生。针对疾病相关的激酶靶标，研发ATP竞争性的小分子激酶抑制剂成为当前的热点。但激酶催化域的结构和序列高度保守，使得许多激酶抑制剂的选择性都比较低。近年来，随着结构生物学和计算机辅助药物分子设计技术的进步，选择性的ATP竞争性激酶抑制剂的研发取得了很大的进展，本文拟就近年来选择性的激酶抑制剂设计研究进展作一综述。     

基于分子对接技术靶向PPARγ的降糖药物虚拟筛选

目的:筛选出特异性靶向PPARγ的配体,为降糖药物的研发提供思路.方法:以核受体PPARγ配体结合区域的晶体结构作为靶点,运用Schrodinger分子对接技术虚拟筛选Drugbank中老药分子化合物,结合打分值以及化合物与靶蛋白受体的相互作用模式优化筛选结果,获得具有降糖潜在活性的小分子化合物.结果:经过SP和XP两...     

小分子PIM激酶抑制剂的研究进展

PIM激酶家族在各类肿瘤中高表达,并对肿瘤的发生发展起着重要的调节作用,闪此PIM激酶有望成为抗癌药物的新靶点,小分子PIM激酶抑制剂具有良好的应用前景。本文从PIM激酶家族蛋门结构、在肿瘤发生发展中的作用途径以及小分子PIM激酶抑制剂的研究进展j个方面进行综述。     

以c-met为靶点的酪氨酸激酶抑制剂的研究进展

c-met作为受体酪氨酸激酶,与肿瘤的发生、发展及转移密切相关,c-met激酶是治疗肿瘤的重要靶点。本文主要综述了c-met激酶的作用机制及近年来报道的一系列不同类型的c-met激酶抑制剂,并详细阐述了小分子c-met激酶抑制剂的构效关系。     

牛蒡苷元抗肿瘤潜在靶点预测及验证

目的研究牛蒡苷元潜在抗肿瘤作用靶点。方法以id Target反向分子对接在线服务器为工具预测牛蒡苷元潜在抗肿瘤作用靶点,采用正向分子对接分析其相互作用,并进一步进行体外分子水平和细胞水平试验验证对接结果。结果反向分子对接结果显示牛蒡苷元能够较好地与肝细胞生长因子受体c-Met结合,预测结合常数Ki值为248 nmol·L-1(PDB ID:3EFK);正向分子对接结果显示牛蒡苷元与靶点存在合理的氢键相互作用;酶联免疫法测定牛蒡苷元100 nmol·L-1水平对受体酪氨酸激酶c-Met酶活抑制率为47.5%;磺酰罗丹明B蛋白染色法测定牛蒡子苷元对非小细胞肺癌细胞株(EBC-1)增殖抑制的IC50值为(201.7±19.8)nmol·L-1。结论 c-Met是牛蒡苷元抗肿瘤作用的潜在靶点。     

酪氨酸激酶抑制剂类新药的研发现状

蛋白激酶在多种疾病,特别是肿瘤发生发展过程中起重要作用,以其为药物靶点的激酶抑制剂则成为近年药物研发的热点。目前已有11个小分子激酶抑制剂上市,其中9个为酪氨酸激酶抑制剂。激酶抑制剂具有高选择性、副作用少的特点。已上市药物已经在慢性粒细胞白血病、非小细胞肺癌、肾细胞癌等多种疾病的治疗中显示出其较传统治疗药物的优越性,部分已成为治疗肿瘤的一线用药。本文对小分子酪氨酸激酶抑制剂类新药的研发现状进行综述。     

Optimization of Compound Ranking for Structure‐Based Virtual Ligand Screening Using an Established FRED–Surflex Consensus Approach

The use of multiple target conformers has been applied successfully in virtual screening campaigns; however, a study on how to best combine scores for multiple targets in a hierarchic method that combines rigid and flexible docking is not available. In this study, we used a data set of 59 479 compounds to screen multiple conformers of four distinct protein targets to obtain an adapted and optimized combination of an established hierarchic method that employs the programs FRED and Surflex. Our study was extended and verified by application of our protocol to ten different data sets from the directory of useful decoys (DUD). We quantitated overall method performance in ensemble docking and compared several consensus scoring methods to improve the enrichment during virtual ligand screening. We conclude that one of the methods used, which employs a consensus weighted scoring of multiple target conformers, performs consistently better than methods that do not include such consensus scoring. For optimal overall performance in ensemble docking, it is advisable to first calculate a consensus of FRED results and use this consensus as a sub‐data set for Surflex screening. Furthermore, we identified an optimal method for each of the chosen targets and propose how to optimize the enrichment for any target.     

In silico design of protein kinase inhibitors: successes and failures

Protein kinases are among the most exploited targets in modern drug discovery due to key roles these enzymes play in human diseases including cancer. The in silico approach, an important part of rational design of protein kinase inhibitors, is founded on vast information about 3D structures of these enzymes. This review summarizes general structural features of the kinase inhibitors and the studies applied toward a large scale chemical database for virtual screening. Analyzed are the ways of validating the modern docking tools and their combinations with different scoring functions. In particular, we discuss the kinase flexibility as a reason for failures of the docking procedure. Finally, evidence is provided for the main patterns of kinase-inhibitor interactions and creation of the hinge-region-directed 2D filters.     

Identification of Novel Selective Lysine‐Specific Demethylase 1 (LSD1) Inhibitors Using a Pharmacophore‐Based Virtual Screening Combined with Docking

Lysine‐specific demethylase 1 (LSD1) plays an important role in regulating the lysine methylation at residues K4 and K9 on histone H3. High levels of LSD1 expression have been observed in several malignant tumors. In this study, we presented a pharmacophore‐based virtual screening of a moderate database of 171 143 small molecules. A pharmacophore of LSD1 inhibitors was constructed for the first time and then used to screen the compound library combined with validated molecular docking tools followed by biochemical assays, led to the identification of 9 novel LSD1 inhibitors, showing their IC₅₀ values in a range of 2.41–101 μm . Furthermore, compound XZ 09 exhibited less inhibition against the homologous monoamine oxidase A (MAO‐A) and B (MAO‐B) displaying its moderate selectivity. Our study provides an effective virtual screening method to identify new LSD1 inhibitors and XZ09 represents a potent and selective lead compound to deserve further optimization for the treatment of LSD1 overexpressing cancers.     

Evaluating reverse docking on general and selective inhibitors: a case study about glide

As a powerful tool for target prediction, reverse docking remains largely unexplored. The objective evaluation of reversedocking software can help us know better about the strength and weakness of these tools, hence guiding us in target prediction. In the present study, we evaluated the target prediction power of Glide (SP) against general inhibitors and selective inhibitors. The results showed that the scoring tendency could be different for each ligand, and overall scoring sampling was necessary for a better understanding of the docking score for a certain protein-ligand pair. Besides, the input conformation of the binding pocket could affect the docking result. Glide (SP) showed a preferable performance on the target prediction of the general inhibitors. However, the accuracy of the target prediction of the selective inhibitors was relatively low, indicating that Glide (SP) might not be capable for this task. The case study about COVID-19 proved that coagulation factor Xa might be a potential target of chloroquine. Therefore, we recommend the further development of reverse docking tools and rectification of inter-target scoring bias.     

Evaluating the Predictivity of Virtual Screening for Abl Kinase Inhibitors to Hinder Drug Resistance

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug‐resistant mutant ABL1‐T315I. ‘Dual active’ inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand‐based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies.     

Virtual screening for kinase targets

Kinases have become a major area of drug discovery and structure-based design. Hundreds of 3D structures for more than thirty different kinases are available to the public. High structural and sequence homology within the kinase gene family makes the remaining kinases ideal targets for homology modeling and virtual screening. Somewhat surprisingly, however, the number of publications about virtual screening of kinases is very low. Therefore, rather than reviewing the field of virtual screening for kinases, we attempt here a hybrid approach of presenting what is known and common practice together with new studies on CDK2 and SRC kinase. To illustrate the challenges and pitfalls of virtual screening for kinase targets we focus on the question of how ranking is influenced by the database screened, the docking scheme, the scoring function, the activity of the compounds used for testing, and small changes in the binding pocket. In addition, a case study of finding irreversible inhibitors of ErbB2 through in silico screening is presented.     

Design,synthesis, and preliminary bioactivity evaluation of N1‐hydroxyterephthalamide derivatives with indole cap as novel histone deacetylase inhibitors

Histone deacetylases inhibitors (HDACIs) have been widely recognized as significant therapeutic approach to cancers. In our efforts to develop novel histone deacetylases inhibitors (HDACIs) as potential anticancer agents, a series of N¹‐hydroxyterephthalamide derivatives with an indole cap group were designed and synthesized. Compound 12m was identified to be the most potent one (IC₅₀ = 0.074 μm against HeLa nuclear extract ) and showed higher inhibitory activity than the positive control SAHA (IC₅₀ = 0.131 μm ), which was also verified by further molecular docking studies into active site of HDAC2. The results of selectivity on the inhibition of HDACs exhibited 12m being with similar isoform selective profile with PXD101. In addition, the representative compounds (8d, 12d, 12j, 12m) based on the outcomes of preliminary tumor cell screening demonstrated more potent or comparable to SAHA in the next antiproliferative activity assays. Collectively, the results encouraged further development of this chemical template to provide more potent analogs as HDACIs.     

Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering

Accounting for protein flexibility is an essential yet challenging component of structure-based virtual screening. Whereas an ideal approach would account for full protein and ligand flexibility during the virtual screening process, this is currently intractable using available computational resources. An alternative is ensemble docking, where calculations are performed on a set of individual rigid receptor conformations and the results combined. The primary challenge associated with this approach is the choice of receptor structures to use for the docking calculations. In this work, we show that selection of a small set of structures based on clustering on binding site volume overlaps provides an efficient and effective way to account for protein flexibility in virtual screening. We first apply the method to crystal structures of cyclin-dependent kinase 2 and HIV protease and show that virtual screening for ensembles of four cluster representative structures yields consistently high enrichments and diverse actives. We then apply the method to a structural ensemble of the androgen receptor generated with molecular dynamics and obtain results that are in agreement with those from the crystal structures of cyclin-dependent kinase 2 and HIV protease. This work provides a step forward in the incorporation of protein flexibility into structure-based virtual screening.     

Discovery of naphthyl‐N‐acylhydrazone p38α MAPK inhibitors with in vivo anti‐inflammatory and anti‐TNF‐α activity

Protein kinases constitute attractive therapeutic targets for development of new prototypes to treat different chronic diseases. Several available drugs, like tinibs, are tyrosine kinase inhibitors; meanwhile, inhibitors of serine/threonine kinases, such as mitogen‐activated protein kinase (MAPK), are still trying to overcome some problems in one of the steps of clinical development to become drugs. So, here we reported the synthesis, the in vitro kinase inhibitory profile, docking studies, and the evaluation of anti‐inflammatory profile of new naphthyl‐N‐acylhydrazone derivatives using animal models. Although all tested compounds ( 3a–d ) have been characterized as p38α MAPK inhibitors and have showed in vivo anti‐inflammatory action, LASSBio‐1824 ( 3b ) presented the best performance as p38α MAPK inhibitor, with IC₅₀ = 4.45 μm , and also demonstrated to be the most promising anti‐inflammatory prototype, with good in vivo anti‐TNF‐α profile after oral administration.     

Inverse docking based screening and identification of protein targets for Cassiarin alkaloids against Plasmodium falciparum

Various reports have shown Cassiarin alkaloids, selective in vitro activities against various strains of Plasmodium falciparum with low cytotoxicity, which indicates their possible candidature as antimalarial drug. However, poor recognition of their protein targets and molecular binding behaviour, certainly limits their exploration as antimalarial drug candidature. To address this, we utilises inverse screening, based on three different docking methodologies in order to find their most putative protein targets. In our study, we screened 1047 protein structures from protein data bank, which belongs to 147 different proteins. Our investigation identified 16 protein targets for Cassiarins. In few cases of identified protein targets, the binding site was poorly studied, which encouraged us to perform comparative sequence and structural studies with their homologous proteins, like as in case of Kelch motif associated protein, Armadillo repeats only protein and Methionine aminopeptidase 1b. In our study, we also found Tryptophanyl-tRNA synthetase and 1-Deoxy-D-Xylose-5-phosphate reductoisomerase proteins are the most common targets for Cassiarins.