HMG-CoA还原酶抑制剂的3D-QSAR研究:基于分子对接和FlexS的叠合

HMG-CoA还原酶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,从而降低心脑血管疾病的发病几率。拜斯亭事件以后,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。本文利用已经建立的分子对接模型对接文献中已经报道的几组HMGR抑制剂分子,确定这些分子可能的结合构象。然后,利用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)研究其三维定量构效关系,所建CoMFA、CoMSIA模型的交叉验证相关系数q~2分别为0.625和0.683(10组CV),对测试集化合物的活性预测结果与实验数据相关性很好,表明模型预测能力较强。分析出三维空间中各种分子场(立体、静电、疏水、氢键)的有利位置。同时,论文还采用FlexS的叠合方式构建CoMSIA模型,比较3D-QSAR研究中分子对接和分子场的叠合。     

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。     

HMG-CoA还原酶的活性位点分析及其抑制剂药效团模型的构建

HMG-CoA还原酶(HMG-CoA Reductase,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,从而降低心脑血管疾病的发病几率。虽然已经开发了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。本论文利用蛋白质活性位点分析程序Grid,分析了HMGR底物结合腔的形状和表面特性,在细致地分析了各类药物与HMGR具体的氢键、疏水相互作用后,结合分子对接、3D-QSAR研究结果,总结了HMGR抑制剂的药效基团模型,并提出了可行的HMGR抑制剂的设计方案,为全新HMGR抑制剂的设计和先导化合物的优化提供了可靠的信息,并对HMGR抑制剂的进一步修饰提出了可行的思路。     

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。     

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。     

基于深度神经网络的药物蛋白虚拟筛选

药物的研发是一种投入成本高、耗费时间长且成功率较低的一种研究,为了在药物开发阶段可以快速获得潜在的化合物,针对性地提出一种基于深度神经网络的药物蛋白虚拟筛选的方法。首先从给定数据集中学习如何提取相关特征,获取配体原子和残基类型进行特征分析,快速识别活性分子和非活性分子,然后使用降维方式和K折验证等方法对药物筛选的模型进行处理,最后通过分析富集因子和AUC值验证诱饵化合物与分子蛋白的互相作用验证模型的可靠程度,实验结果表明所提出的筛选方法具有很好的可行性和有效性,有效地加快了虚拟筛选过程。     

抗药物依赖药效团模型的构建与虚拟筛选

运用计算机辅助药物设计方法,基于多巴胺D2和D3受体部分激动剂构建具有抗药物依赖功效的药效团模型。再以该药效团模型作为提问结构,在天然产物数据库中进行虚拟筛选,利用分子对接技术,对筛选结果进行分析和评价,初步得到具有抗药物依赖功效的目标化合物。最后对目标化合物进行虚拟水溶性、肠道吸收性和血脑屏障通透性研究,为抗药物依赖新药的研发提供理论基础。     

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JPPF的异构分布式MTH1虚拟筛选系统

随着癌症发病率日益升高,寻找治疗癌症的新靶点已成为世界范围内的研究热点。最新研究指出MTH1蛋白在癌细胞中生存是必须的,而在正常细胞中是非必需的,设计有选择性的MTH1抑制剂将对癌症的治疗有着重要的意义。MTH1的筛选需要大规模的高性能计算资源,但目前缺少具体成型的集筛选模拟于一体、跨平台的分布式异构软件以用于快速地从大规模数据库中获得潜在的候选药物小分子。本文基于JPPF分布式并行框架和Autodock Vina设计一种具有良好兼容性和跨平台性的肿瘤药物虚拟筛选计算系统,通过对100万目标分子集进行虚拟筛选,筛选结果直接靶向了MTH1的药物分子。该系统的实现为快速构建大规模药物分子虚拟筛选技术提供了解决方案和新思路。     

酪氨酸激酶抑制剂的虚拟受体模型方法研究

利用柔性原子受体模型(FLARM)方法对一系列的异黄酮和喹诺酮衍生物表皮生长因子受体酪氨酸激酶抑制剂进行了三维定量构效关系研究,得到了合理的构效关系模型。FLARM方法的计算结果还给出了虚拟的受体模型,该模型说明了抑制剂与受体之间可能的相互作用。这些相互作用包括两个氢键和一个硫-芳香相互作用,以上结果仅仅是由7个化合物得到的。     

Ensemble docking-based virtual screening yields novel spirocyclic JAK1 inhibitors

Small molecule inhibition of Janus kinases (JAKs) has been demonstrated as a viable strategy for the treatment of various inflammatory conditions and continues to emerge in cancer-related indications. In this study, a large supplier database was screened to identify novel chemistry starting points for JAK1. The docking-based screening was followed up by testing ten hit compounds experimentally, out of which five have displayed single-digit micromolar and submicromolar IC₅₀ values on JAK1. Thus, the study was concluded with the discovery of five novel JAK inhibitors from a tiny screening deck with a remarkable hitrate of 50%. The results have highlighted spirocyclic pyrrolopyrimidines with submicromolar JAK1 IC₅₀ values and a preference for JAK1 over JAK2 as potential starting points in developing a novel class of JAK1 inhibitors.     

Virtual screening of novel reversible inhibitors for marine alkaline protease MP

Marine alkaline protease (MP,² accession no. ACY25898) is produced by a marine bacterium strain isolated from Yellow Sea sediment in China. Previous research has shown that this protease is a cold-adapted enzyme with antioxidant activity that could be used as a detergent additive. Owing to its instability in the liquid state, MP's application in liquid detergents was limited. Therefore, the discovery of reversible MP inhibitors to stabilize the protease was imperative. Here, we used the X-ray structure of MP and recompiled AutoDock 4.2 with refined Zn²⁺ characters to screen the free chemical database ZINC. After completing the docking procedure, we applied strategies including the “initial filter”, consensus scoring and pharmocophore model to accelerate the process and improve the virtual screening success rate. The “initial filter” was built based on the docking results of boronic acid derivatives validated as reversible inhibitors of MP by our previous studies. Finally, ten compounds were purchased or synthetized to test their binding affinity for MP. Three of the compounds could reversibly inhibit MP with apparent K_i values of 0.8–1.2 mmol. These active compounds and their binding modes provide useful information for understanding the molecular mechanism of reversible MP inhibition. The results may also serve as the foundation for further screening and design of reversible MP inhibitors.     

Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors

Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes involved in protein biosynthesis in all living organisms and are an unexploited antibacterial targets, as many strains of bacteria have become resistant to all established classes of antibiotics. Therefore, the main aim of this study is to discover new lead molecules which would be useful as anti-bacterial compounds. Pharmacophore models were developed by using CATALYST HypoGen with a training set of 29 diverse methionyl-tRNA synthetase (MetRS) inhibitors. The best quantitative pharmacophore hypothesis (Hypo1) obtained a correlation coefficient of 0.975, root mean square deviation (RMSD) of 0.55 and cost difference (null cost-total cost) of 70.32. This Hypo1 was validated by two methods, first by using 104 test set molecules which resulted a correlation of 0.926 between HypoGen estimated activities versus experimental activities and secondly by Cat-Scramble validation method. This validated pharmacophore model was further used for screening databases for discovery of new MetRS inhibitors. The new lead compounds were further analyzed for drug-like properties. Homology modeled structure of Staphylococcus aureus MetRS was built and molecular docking studies were performed with many inhibitors using the newly built protein structure. Finally, it was found that the new leads exhibited good estimated inhibitory activity, calculated binding properties similar to experimentally proven compounds and also favorable drug-like properties.     

Identification of phenoxyacetamide derivatives as novel DOT1L inhibitors via docking screening and molecular dynamics simulation

Dot1-like protein (DOT1L) is a histone methyltransferase that has become a novel and promising target for acute leukemias bearing mixed lineage leukemia (MLL) gene rearrangements. In this study, a hierarchical docking-based virtual screening combined with molecular dynamic (MD) simulation was performed to identify DOT1L inhibitors with novel scaffolds. Consequently, 8 top-ranked hits were eventually identified and were further subjected to MD simulation. It was indicated that all hits could reach equilibrium with DOT1L in the MD simulation and further binding free energy calculations suggested that phenoxyacetamide-derived hits such as L01, L03, L04 and L05 exhibited remarkably higher binding affinity compared to other hits. Among them, L03 showed both the lowest glide score (−12.281) and the most favorable binding free energy (−303.9 +/− 16.5 kJ/mol), thereby making it a promising lead for further optimization.     

A systematic methodology for large scale compound screening: A case study on the discovery of novel S1PL inhibitors

Decrease in sphingosine 1-phosphate (S1P) concentration induces migration of pathogenic T cells to the blood stream, disrupts the CNS and it is implicated in multiple sclerosis (MS), a progressive inflammatory disorder of the central nervous system (CNS), and Alzheimer’s disease (AD). A promising treatment alternative for MS and AD is inhibition of the activity of the microsomal enzyme sphingosine 1-phosphate lyase (S1PL), which degrades intracellular S1P. This report describes an integrated systematic approach comprising virtual screening, molecular docking, substructure search and molecular dynamics simulation to discover novel S1PL inhibitors. Virtual screening of the ZINC database via ligand-based and structure-based pharmacophore models yielded 10000 hits. After molecular docking, common substructures of the top ranking hits were identified. The ligand binding poses were optimized by induced fit docking. MD simulations were performed on the complex structures to determine the stability of the S1PL-ligand complex and to calculate the binding free energy. Selectivity of the selected molecules was examined by docking them to hERG and cytochrome P450 receptors. As a final outcome, 15 compounds from different chemotypes were proposed as potential S1PL inhibitors. These molecules may guide future medicinal chemistry efforts in the discovery of new compounds against the destructive action of pathogenic T cells.     

一个面向虚拟网络拓扑发现系统的设计与实现

伴随当前虚拟化技术的成熟及应用,很多企业和单位开始尝试应用虚拟化技术,因此,对部门内部虚拟网络高效管理的需求变得尤为突出,而网络拓扑信息又是网络管理的先决条件,在对目前网络拓扑自动发现技术深入分析研究的基础上,本文设计并实现了一个面向虚拟网络的物理拓扑自动发现系统,它综合了多种拓扑发现机制和技术的优势,具有设计复杂度低,发现拓扑结构快速和完整的特点。