乙酰胆碱M3受体拮抗剂的从头设计

M3受体是人体内分布广泛的一种重要的毒蕈碱乙酰胆碱受体亚型,与膀胱过度活动症、心律失常、呼吸道及支气管疾病密切相关.设计与合成M3受体拮抗剂对于开发治疗相关疾病的药物具有重要理论意义和应用价值。本文采用同源模建方法,构建了人M3受体蛋白的三维结构,基于M3受体拮抗剂的结构构建了其药效团模型,其中较理想的模型含有6个药效团特征元素。利用药效团模型进行了虚拟筛选,虚拟筛选的数据库是本实验室构建的虚拟化合物库,发现了10个新型的对M3受体有拮抗作用的化合物。最后对这10个化合物进行了分子对接,根据对接结果,发现3个化合物对接能量及结合方式比较合理,为M3受体拮抗剂的合成研究及活性测定奠定了基础。     

基于结构的组蛋白去乙酰化酶抑制剂虚拟筛选

组蛋白去乙酰化酶是抗肿瘤作用的新靶点,基于该酶复合物的三维结构,首先对具有分子多样性的数据库进行了虚拟筛选;然后根据已知HDAC抑制剂的结构特征和筛选的结果,以及与生物大分子互补性,选择合理的构建单元,组建靶向的虚拟组合库;最后进行数据库虚拟筛选,对分子对接的结果进行评分,选择出理论上与HDAC有较好结合能力的化合物,设计了酰胺类、脲类和酰肼类全新结构类型的HDAC抑制剂,初步生物活性评价结果表明,预期有生物活性的化合物显示出一定的HDAC酶抑制活性。     

HMG-CoA还原酶抑制剂的虚拟筛选模型的建立

HMG-CoA还原酶(简称HMGR)是降血脂药物设计的重要靶标,抑制该酶的活性可以有效地降低血浆总胆固醇水平,从而降低罹患心脑血管疾病的几率。虽然已经有数种他汀类药物作为HMGR抑制剂应用于临床,但是他汀类药物的安全性,特别是长期服用的安全性一直备受关注,所以设计新型安全的HMGR抑制剂仍然十分迫切。论文根据hHMGR的晶体结构,利用Dock、FlexX、Autodock 3个程序建立了hHMGR抑制剂的虚拟筛选模型,模型的可靠性通过重复晶体结构、对比对接打分和化合物的活性之间的相关性等方法得以验证,最后,分析各种对接软件的特点,指出了hHMGR抑制剂虚拟筛选,特别是目筛中应当注意的问题,以期为新型HMGR抑制剂的设计提供指导。     

苯甲酰氨类HDAC2抑制剂的3D-QSAR及虚拟筛选研究

本文应用传统比较分子力场分析法CoMFA,比较分子相似性指数法CoMSIA和Topomer CoMFA方法,对组蛋白去乙酰化酶2(HDAC2)的苯甲酰胺类抑制剂进行了构效关系和基于药效团的筛选研究。基于分子片段建模的Topomer CoMFA的交叉验证系数q~2为0.594,预测相关系数r~2_(pred)为0.973。基于对接活性构象叠合得到的CoMFA,CoMSIA的交叉验证相关系数q~2分别为0.634,0.561,预测相关系数r~2_(pred)分别为0.905,0.68。基于药效团模型011叠合的CoMFA,CoMSIA交叉验证相关系数q~2分别为0.588,0.592,预测相关系数r~2_(pred)分别为0.68,0.859。结果表明这5个3D-QSAR模型均具有良好的稳定性和预测能力。另外,由18个活性较高结构多样的分子建立了可靠的药效团模型。运用药效团模型011和016对NCI数据库进行筛选,将筛选得到的分子与HDAC2蛋白酶进行分子对接,并由PASS进行活性验证,最终得到了18个分子,且对接打分值都大于6,可作为新的HDAC2抑制剂。     

乙酰胆碱酯酶抑制剂虚拟筛选方法研究

在虚拟筛选过程中，虚拟筛选策略和方法是获取结果的基础，但需要通过实验数据来检验。获得虚拟筛选合理的限制因素，将为大规模虚拟筛选提供方法依据。通过建立乙酰胆碱酯酶抑制剂的活性检测方法，检测了4000多个化合物的生物活性，并发现了34个活性较高的化合物，同时将设计的6个不同的虚拟筛选方法分别用于2个乙酰胆碱醋酶抑制剂虚拟筛选模型。将所预测的可能活性化合物及两模型共有的可能活性化合物分别与实验所得的活性化合物对照，综合分析讨论虚拟筛选乙酰胆碱酯酶抑制剂的重要因素和进一步富集活性化合物的方法，为大规模的虚拟筛选乙酰胆碱醋酶抑制剂提供可靠依据，并为其他基于蛋白酶结构的虚拟筛选提供参考。     

基于骨架结构分类法的递进式药物筛选

本文报告一种新颖的基于骨架结构分类法的递进式药物筛选(PS-SCA)技术。此技术在美国国立癌症研究所(NCI)的八组细胞水平上的高通量筛选实验数据上进行了测试,结果表明,基于拓扑结构数据的递进式筛选可以极大地降低药物筛选的代价,缩短筛选时间。模拟实验证明,PS-SCA递进式筛选技术包括三个阶段:(1)骨架多样性采样筛选试验:(2)活性化合物发现试验;(3)可忽略的多余筛选试验。运用PS-SCA递进式筛选技术,可以在只筛选20％的化合物情况下找到最有意义的70-80％的活性化合物。而且,这70％-80％的活性化合物中包含了关键的结构骨架。     

DDGrid:一种大规模药物虚拟筛选网格

化合物活性筛选是创新药物研究的起点和具有决定意义的步骤,利用网格计算技术进行药物虚拟筛选能够极大提高药物筛选的有效性,同时可以大量减少新药研制的成本和时间。新药研发网格DDGrid是中国国家网格CNGrid的重要支持项目,通过实施主从模式架构并在网格资源监控中使用适配器模式,DDGrid可以对超过10万规模的化合物分子数据库进行虚拟筛选。     

基于分子参数的CYP2C9抑制剂的预测模型

细胞色素P4502C9(cytochrome P4502C9,CYP2C9)是肝脏重要的一种异物质代谢酶,许多药物或化学物质均可抑制和干扰其活性,在某种药物发现早期,预测基于CYP2C9抑制的药-药相互作用对筛选及发现新药具有重要意义。本文旨在建立CYP2C9抑制剂的预测模型,并确定抑制剂和非抑制剂显著不同的参数。选择81个化合物作为数据集,随机选其中64个为训练集,其余为验证集;选取250个分子参数给化合物数字化。采用逐步判别分析法(stepwise discriminant analysis method)和K-均值聚类分析法(K-Means cluster analysis method)模拟,建立数学模型,并用验证集检验模型的预测能力。结果表明:训练集的抑制剂正确率为96.4%,非抑制剂为97.2%;验证集的抑制剂正确率为85.7%,非抑制剂为90.0%。而采用K-均值聚类法时,抑制剂和非抑制剂的正确率也分别达到了82.9%和86.9%。对结果的深入分析找出对该模型贡献较大的参数为分子中氨基、烯基基团电拓扑状态指数、碳环数量以及疏水性参数,那些参数对区分抑制剂和非抑制剂两种结构差异、帮助指导CYP2C9抑制剂的筛选和发现具有重要意义。     

新型抗流感病毒药物——三羟基甲氧基黄酮分子对接的研究

运用柔性分子对接程序Affinity,深入研究了5,7,4'-三羟基-8-甲氧基黄酮(MF)与N1、N9亚型神经氨酸酶之间的结合方式并阐明其作用机制.结果表明MF与N1亚型神经氨酸酶之间有一种作用模式,其结合能为-70.26 kcal·mol-1,而与N9亚型之间存两种竞争性的结合模式,最大结合能为-83.51 kcal·mol-1.进一步分析发现MF与这两种亚型神经氨酸酶相互作用的作用力类型、氢键作用及关键作用的氨基酸残基等有着明显的区别.现行药物奥斯米韦作用模式单一,MF则可以与各种亚型甚至变异的神经氨酸酶发生很好地相互作用.因此,MF是一种极具应用前景的新型抗流感病毒药物.结合前人的研究成果,本研究提出了以MF为底物的流感药物修饰方向.     

对Rac1蛋白抑制作用的生物还原剂的虚拟筛选

乏氧是肿瘤组织的重要特点,而在肿瘤组织中高表达的 Rac1蛋白是与氧化还原过程密切相关的蛋白质,且参与肿瘤发展的多个过程。Rac1抑制剂可有效抑制肿瘤细胞的侵润与发展。因此,有效的Rac1抑制剂可成为潜在抗癌药物。目前能用于临床治疗肿瘤的 Rac1抑制剂鲜有报道。生物还原剂是一大类对乏氧细胞有特异毒性的化合物,可以靶向作用于乏氧细胞,抑制乏氧肿瘤细胞的生长。本文根据Rac1的三维结构,利用虚拟筛选软件PyRx,在化合物库中对约1.5万个硝基杂环化合物、芳香氮氧化合物、脂肪氮氧化合物、醌类化合物等生物还原剂进行筛选,根据结合能、结合位点、结合模式等条件筛选出6个候选Rac1抑制剂,为靶向乏氧肿瘤细胞的生物还原剂的开发、构效关系研究提供理论依据。     

Exploration of DPP-IV inhibitors with a novel scaffold by multistep in silico screening

Dipeptidyl peptidase-IV (DPP-IV), an enzyme that degrades incretins–hormones that promote insulin secretion–is a therapeutic target for type 2 diabetes, with a number of its inhibitors having been launched as therapies for diabetes. Since adverse effects of these inhibitors have recently been reported, the development of novel DPP-IV inhibitors with higher efficacy and safety is required. We, therefore, screened for novel DPP-IV inhibitors using the combination of an in silico drug discovery technique and a DPP-IV assay system. We initially selected seven candidate compounds as DPP-IV inhibitors from a database consisting of four million compounds by a multistep in silico screening procedure combining pharmacophore-based screening, docking calculation and the analysis of three-dimensional quantitative structure-activity relationship. We then measured the inhibitory activity of the selected compounds and identified a hit compound. In addition, we discuss the structure–activity relationship between the binding mode model and inhibitory activity of the hit compound.     

Structure based virtual screening-driven identification of monastrol as a potent urease inhibitor

Virtual screening uses computer based methods to discover new ligands on the basis of biological structures. Among all virtual screening methods structure based docking has received considerable attention. In an attempt to identify new ligands as urease inhibitors, structure-based virtual screening (SBVS) of an in-house database of 10,000 organic compounds was carried out. The X-ray crystallographic structure of Bacillus pasteurii (BP) in complex with acetohydroxamic acid (PDB Code 4UBP) was used as a protein structure. As a starting point, ~10,000 compounds of our in-house database were analyzed to check redundancy and the compounds found repeated were removed from the database. Finally 6993 compounds were docked into the active site of BP urease using GOLD and MOE-Dock software. A remarkable feature of this study was the identification of monastrol, a well-known KSP inhibitor already in clinical trials, as a novel urease inhibitor. The hits identified were further evaluated by molecular docking and on examination of the affinity predictions, twenty-seven analogs of monastrol were synthesized by a multicomponent Biginelli reaction followed by their in vitro screening as urease inhibitors. Finally twelve compounds were identified as new urease inhibitors. The excellent in vitro activity suggested that these compounds may serve as viable lead compounds for the treatment of urease related problems.     

Identification of p38α MAP kinase inhibitors by pharmacophore based virtual screening

The p38α mitogen-activated protein (MAP) kinase plays a vital role in treating many inflammatory diseases. In the present study, a combined ligand and structure based pharmacophore model was developed to identify potential DFG-in selective p38 MAP kinase inhibitors. Conformations of co-crystallised inhibitors were used in the development and validation of ligand and structure based pharmacophore modeling approached. The validated pharmacophore was utilized in database screening to identify potential hits. After Lipinski's rule of five filter and molecular docking analysis, nineteen hits were purchased and selected for in vitro analysis. The virtual hits exhibited promising activity against tumor necrosis factor-α (TNF-α) with 23–98% inhibition at 10 μM concentration. Out of these seven compounds has shown potent inhibitory activity against p38 MAP kinase with IC₅₀ values ranging from 12.97 to 223.5 nM. In addition, the toxicity study against HepG2 cells was also carried out to confirm the safety profile of identified virtual hits.     

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4–78.0%, 4.7–73.8%, and 214–10,543, respectively, compared to those of 62–95%, 0.65–35%, and 20–1200 by structure-based VS and 55–81%, 0.2–0.7%, and 110–795 by other ligand-based VS tools in screening libraries of ≥1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3–87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.     

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4–78.0%, 4.7–73.8%, and 214–10,543, respectively, compared to those of 62–95%, 0.65–35%, and 20–1200 by structure-based VS and 55–81%, 0.2–0.7%, and 110–795 by other ligand-based VS tools in screening libraries of ≥1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3–87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.     

Structure-based drug design to the discovery of new 2-aminothiazole CDK2 inhibitors

N-(5-Bromo-1,3-thiazol-2-yl)butanamide (compound 1) was found active (IC50=808 nM) in a high throughput screening (HTS) for CDK2 inhibitors. By exploiting crystal structures of several complexes between CDK2 and inhibitors and applying structure-based drug design (SBDD), we rapidly discovered a very potent and selective CDK2 inhibitor 4-[(5-isopropyl-1,3-thiazol-2-yl)amino] benzenesulfonamide (compound 4, IC50=20 nM). The syntheses, structure-based analog design, kinases inhibition data and X-ray crystallographic structures of CDK2/inhibitor complexes are reported.     

Multiple target screening method for robust and accurate in silico ligand screening

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].