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。     

基于图卷积神经网络的小分子虚拟筛选

新药研发存在研发周期长、成本高和成功率低等问题。为了解决这一系列问题,提高早期药物研发效率,提出一种基于图卷积神经网络的虚拟筛选方法,并利用模型对EGFR(Epidermal Growth Factor Receptor,表皮生长因子受体)靶点进行虚拟筛选。首先获取EGFR靶点的相关数据,对其进行数据处理后用于模型训练;随后应用模型筛选大量化合物,筛选出小分子后,将其与药物分子进行化合物相似性搜索,验证其是否与已知的EGFR药物存在相似性;同时,将图卷积神经网络(Graph Convolutional Networks,GCN)模型与其他传统机器学习模型进行比较,证明本研究模型在各项指标中均优于其他模型。实验结果表明,本研究提出的方法具有较好的预测性和准确性,为发现潜在药物提供了助力。     

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.     

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。     

Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening

Docking-based virtual screening is an established component of structure-based drug discovery. Nevertheless, scoring and ranking of computationally docked ligand libraries still suffer from many false positives. Identifying optimal docking parameters for a target protein prior to virtual screening can improve experimental hit rates. Here, we examine protocols for virtual screening against the important but challenging class of drug target, protein tyrosine phosphatases. In this study, common interaction features were identified from analysis of protein–ligand binding geometries of more than 50 complexed phosphatase crystal structures. It was found that two interactions were consistently formed across all phosphatase inhibitors: (1) a polar contact with the conserved arginine residue, and (2) at least one interaction with the P-loop backbone amide. In order to investigate the significance of these features on phosphatase-ligand binding, a series of seeded virtual screening experiments were conducted on three phosphatase enzymes, PTP1B, Cdc25b and IF2. It was observed that when the conserved arginine and P-loop amide interactions were used as pharmacophoric constraints during docking, enrichment of the virtual screen significantly increased in the three studied phosphatases, by up to a factor of two in some cases. Additionally, the use of such pharmacophoric constraints considerably improved the ability of docking to predict the inhibitor's bound pose, decreasing RMSD to the crystallographic geometry by 43% on average. Constrained docking improved enrichment of screens against both open and closed conformations of PTP1B. Incorporation of an ordered water molecule in PTP1B screening was also found to generally improve enrichment. The knowledge-based computational strategies explored here can potentially inform structure-based design of new phosphatase inhibitors using docking-based virtual screening.     

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.     

The influence of self-discrepancy between the virtual and real selves in virtual communities

Virtual communities enable one to pretend to be a different person or to possess a different self-identity at little or no cost. Despite the ubiquity of such communities, there is limited theoretical and empirical research regarding the effect of taking on a different self-identity associated with one’s psychological and behavioral functioning in those communities. To address this issue, drawing on the self-concept rooted in sociopsychology, this study employs the self-discrepancy index, which assesses the degree of differences between one’s virtual and real selves; the study goes onto develop a theoretical framework that links self-discrepancy, psychological states (i.e., autonomy, recovery, and catharsis), and behavior (i.e., contribution quality and quantity). The results of an analysis involving 299 survey participants show that self-discrepancy has a significant influence on autonomy and recovery and that this, in turn, influences levels of contribution quality and quantity. It is of note that the results of this study indicate that catharsis is inversely related to contribution quality. Furthermore, subgroup analysis reveals that the effects of self-discrepancy on contribution vary depending on whether the virtual community is utilitarian or hedonic.