基于药效团模型的DHODH抑制剂构效关系研究

利用药效团模型研究二氢乳清酸脱氢酶(Dihydroorotate dehydrogenase,DHODH)抑制剂的构效关系,为DHODH抑制剂的虚拟筛选提供新的方法.以31个具有DHODH抑制活性的化合物为训练集化合物,半数抑制浓度(IC50)范围为7~63000 nmol/L,利用Catalyst/HypoGen算法构建DHODH抑制剂药效团模型,通过对训练集化合物多个构象进行叠合,提取药效团特征及三维空间限制构建药效团模型.利用基于CatScramble的交叉验证方法及评价模型对已知活性化合物的活性预测能力,确定较优药效团模型.模型包含1个氢键受体、3个疏水中心,表征了受体配体相互作用时可能发生的氢键相互作用、疏水相互作用和π-π相互作用,4个药效特征在三维空间的排列概括了DHODH抑制剂产生活性的结构特点.所得较优模型对训练集化合物及测试集化合物的计算活性值与实验活性值的相关系数分别为0.8405和0.8788.利用药效团模型对来源于微生物的系列化合物进行虚拟筛选,筛选出59个预测活性较好的化合物,可作为进一步药物研发的候选化合物.     

人类免疫缺陷病毒整合酶二酮酸类抑制剂的三维药效团构建

应用遗传算法相似性程序(GASP), 以作用于I型人类免疫缺陷病毒(human immun-odeficiency virus type 1, HIV-1)整合酶(IN)的二酮酸类(diketoacids, DKAs)抑制剂构建药效团模型. 所选训练集分子均具有可靠的类药性特征及DKAs药效团特征. 尝试将抑制剂与药效团叠合后的构象和抑制剂与IN的对接构象进行叠合, 得到药效团模型与分子对接构象中IN残基的相对位置, 并基于抑制剂的药效团模型特征与周围IN氨基酸残基位置的匹配情况进行药效团特征的修改. 所得最优药效团由1个疏水特征、3对氢键特征和1个氢键供体特征组成. 该药效团的命中物质量(goodness of hit, GH)为0.56, 产出率(Y)达63.6%, 假阳性率(FP)为0.41%. 该药效团具有较好的置信度, 产出率较高而假阳性率较低, 可用于数据库搜索发现新的具有DKAs药效团特征的活性化合物, 也可为先导化合物的改造提供帮助.     

GPR40激动剂作用方式及药效团模型研究

为阐明GPR40与其激动剂分子之间的相互作用方式,构建可用于GPR40激动剂分子先导化合物筛选的药效团模型。我们利用分子对接技术将GPR40与其激动剂分子进行对接,分析分子与受体之间相互作用的关键氨基酸和结合方式,采用药团模型法分别构建了基于受体-配体复合物(CBP)和基于激动剂分子共同特征(HipHop)的药效团模型,HipHop模型采用测试集法进行验证。结果显示GPR40与小分子相互作用的关键氨基酸主要有ARG183、TYR91、TYR2240、ARG2258、PHE142等,相互作用方式则主要为氢键、盐桥、Pi-Pi Stacking以及疏水作用,以药团模型法构建了10个HipHop模型,其中8号药效团为最优模型,可用于GPR40激动剂分子的虚拟筛选研究,这为GPR40激动剂药物分子设计奠定了理论基础。     

p53-MDM2结合抑制剂药效团模型的构建

采用Catalyst软件, 选择5类共24个p53-MDM2结合抑制剂作为训练集, 经计算机建模、构象优化, 由Catalyst系统构建出药效团模型, 并对药效团进行有效性分析, 结合已知的p53-MDM2结合抑制剂的结构信息, 筛选得到含有一个芳环中心、三个疏水中心和一个氢键受体的具有较好预测能力(Correl=0.941, Config=17.530, 吟cost=150.830)的药效团模型.     

β分泌酶抑制剂的药效团模型研究

选择活性跨越0.002至25 μmol•L－1的4类共25个β分泌酶抑制剂作为训练集, 使用Catalyst软件包构建出药效团模型, 并通过对药效团的有效性分析, 筛选得到的最佳模型(correlCorrel＝0.969, Config＝16.32, Δcost＝62.422)由一个环芳香性、一个疏水中心、一个正电荷中心和一个氢键供体组成. 并用其它209个抑制剂组成测试集对模型进行验证, 结果表明该模型显示出较强的预测能力, 能够为进一步的数据库搜索, 寻找新型的β分泌酶抑制剂先导物提供依据.     

基于药效团模型从中草药数据库中搜索gpIIb/IIIa受体抑制剂

选择20 个3,4-二氢-1(1H)-异喹啉酮类gpIIb/IIIa受体抑制剂作为训练集, 利用Catalyst软件包建立了gpIIb/IIIa受体抑制剂三维药效团模型. 探讨了药效团作用模式. 并通过建立的可靠性最佳的药效团模型(线性回归系数r=0.7715), 从中草药数据库中虚拟筛选了gpIIb/IIIa受体抑制剂, 通过实验活性测定得到了8个抑制ADP活化全血血小板聚集的IC50从40到100 μmol·L-1的化合物, 进一步证明了所建药效团模型的有效性.     

糖原合成酶激酶抑制剂模型的建立

选取四类共89种活性较高的糖原合成酶激酶（GSK-3）抑制剂作为分子训练集，利用CATALYST系统，经构象分析，分子叠合等过程构建出药效团模型。筛选出具有一个氢键受体，一个芳香疏水中心，两个环芳香性的药效团模型 (weight=2.4,RMS=0.50, null cost- fixed cost =104, correlation coefficient=0.95)。该模型具有较强的预测活性能力，可用于优化分子结构，找到高效低毒的化合物。     

基于药效团模型从中草药数据库中搜索gpⅡb/Ⅲa受体抑制剂

选择20个3,4-二氢-1(1H)-异喹啉酮类gpⅡb/Ⅲa受体抑制剂作为训练集,利用Catalyst软件包建立了gpⅡb/Ⅲa受体抑制剂三维药效团模型.探讨了药效团作用模式.并通过建立的可靠性最佳的药效团模型(线性回归系数r=0.7715),从中草药数据库中虚拟筛选了gpⅡb/Ⅲa受体抑制剂,通过实验活性测定得到了8个抑制ADP活化全血血小板聚集的IC50从40到100μmol·L-1的化合物,进一步证明了所建药效团模型的有效性.     

具有全新机理的DNA旋转酶抑制剂的筛选及抑菌活性

利用具有新机制的抗耐药菌DNA旋转酶抑制剂GSK299423与DNA旋转酶的晶体复合物(PDB code:2XCS)构建基于配体-受体复合物的药效团模型, 诱骗集(Decoy set)验证结果表明该药效团模型具有较强的活性识别能力. 将药效团模型与分子对接相结合用于筛选化合物库, 通过抑菌活性测定, 获得了具有抗多药耐药菌活性的DNA旋转酶抑制剂LTH02.     

EGFR酪氨酸激酶抑制剂的药效团模型构建

以80个作用方式相同, 分子结构特征不同的表皮生长因子受体酪氨酸激酶(EGFR TK)竞争性抑制剂为训练集, 利用计算机药物辅助软件Catalyst, 构建不同的药效团模型, 并结合酪氨酸激酶的作用位点等因素, 筛选出一个含有两个芳环中心, 一个疏水中心和一个阳离子基团的具有较好预测能力(RMS=0.438, Correl=0.908, Weight=1.52, Config=17.36)的药效团模型, 为设计和合成新型结构的EGFR TK抑制剂提供参考.     

Modeling ligand-receptor interaction for some MHC class II HLA-DR4 peptide mimetic inhibitors using several molecular docking and 3D QSAR techniques

The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors.     

Studies on New Steroidal Saponins from Allii macrostemonis Bulbus and Their Antitumor Activities Design and Synthesis of Novel Bispecific Molecules for InducingBRD4 Protein Degradation

Proteolysis targeting chimeras(PROTACs) are bispecific molecules containing a target protein binder and a ubiquitin ligase binder connected by a linker. Recently, some heterobifunctional small molecule bromodomain-containing protein 4(BRD4) degraders based on the concept of PROTACs were designed to induce the degradation of BRD4 protein. Herein, we synthesized a new class of PROTAC BRD4 degraders. One of the most promising compound 22f exhibited robust potency of BRD4 inhibition with IC₅₀ value of (9.4±0.6) nmol/L. Furthermore, compound 22f potently inhibited cell proliferation in BRD4-sensitive cell lines RS4;11 with IC₅₀ value of (27.6±1.6) nmol/L and capable of inducing degradation of BRD4 protein at 0.5―1.0 μmol/L in the RS4;11 cells. These data establish that compound 22f is a potent and efficacious BRD4 degrader.     

基于虚拟筛选和分子动力学模拟发现新型ULK1抑制剂

Unc-51样自噬激活激酶1（unc-51-like autophagy activating kinase 1,ULK1）作为自噬启动的重要调控因子,是肿瘤治疗的关键靶点之一。首先,以已知ULK1抑制剂为基础构建药效团模型,通过药效团模型筛选、分子对接以及分子力学广义波恩表面积（Molecular Mechanics/Generalized Born Surface Area,MM/GBSA）结合自由能计算等方法,对含有52万多个类药性小分子的数据库进行虚拟筛选,得到具有较高理论亲和力的化合物。随后,50ns的分子动力学模拟验证了蛋白质-配体复合物结合的稳定性,最后10ns的平均结合自由能的计算研究进一步验证了配体的结合能力。结果表明,6个化合物（F5258-0159、F3407-0428、F0529-1100、F0696-3531、F3222-5280、F6525-5596）具有骨架新颖、分子对接分数和结合自由能数值优异及与ULK1的结合状态稳定等特点,可以作为新型潜在的ULK1抑制剂用于肿瘤治疗的研究,也为新型ULK1抑制剂的设计和研发提供新的研究思路。     

Predicting hiCE inhibitors based upon pharmacophore models derived from the receptor and its ligands

Human intestinal carboxyl esterase (hiCE) is a drug target for ameliorating irinotecan-induced diarrhea. By reducing irinotecan-induced diarrhea, hiCE inhibitors can improve the anti-cancer efficacy of irinotecan. To find effective hiCE inhibitors, a new virtual screening protocol that combines pharmacophore models derived from the hiCE structure and its ligands has been proposed. The hiCE structure has been constructed through homology techniques using hCES1’s crystal structure. The hiCE structure was optimized via molecular dynamics simulations with the most known active hiCE inhibitors docked into the structure. An optimized pharmacophore, derived from the receptor, was then generated. A ligand-based pharmacophore was also generated from a larger set of known hiCE inhibitors. The final hiCE inhibitor predictions were based upon the virtual screening hits from both ligand-based and receptor-based pharmacophore models. The hit rates from the ligand-based and receptor-based pharmacophore models are 88% and 86%, respectively. The final hit rate is 94%. The two models are highly consistent with one another (85%). This proves that both models are reliable.     

基于虚拟筛选策略发现新型潜在JAK3小分子抑制剂

利用已知活性的分子采用基于配体的策略构建药效团模型,通过基于类药规则、药效团模型、多种精度的分子对接算法、MM/GBSA结合能预测以及ADMET筛选手段对含约250万个分子的数据库进行虚拟筛选。发现5种JAK3抑制剂的新型骨架,其中6个以1-苯基咪唑烷-2-酮为骨架的分子在与JAK3激酶的结合能以及分子的ADMET性质评价方面均表现优异,具备高JAK3抑制剂潜力,被认为是虚拟筛选的命中分子。     

Pharmacophore modeling coupled with molecular dynamic simulation approach to identify new leads for meprin-β metalloprotease

Human meprin beta metalloprotease, a small subgroup of the astacin family, is a potent drug target for the treatment of several disorders such as fibrosis, neurodegenerative disease in particular Alzheimer and inflammatory bowel diseases. In this study, a ligand-based pharmacophore approach has been used for the selection of potentially active compounds to understand the inhibitory activities of meprin-β by using the sulfonamide scaffold based inhibitors. Using this dataset, a pharmacophore model (Hypo1) was selected on the basis of a highest correlation coefficient (0.959), lowest total cost (105.89) and lowest root mean square deviation (1.31 Å) values. All the pharmacophore hypotheses generated from the candidate inhibitors comprised four features: two hydrogen-bond acceptor, one hydrogen-bond donor and one zinc binder feature. The best validated pharmacophore model (Hypo1) was used for virtual screening of compounds from several databases. The selective hit compounds were filtered by drug likeness property, acceptable ADMET profile, molecular docking and DFT study. Molecular dynamic simulations with the final 10 hit compounds revealed that a large number of non-covalent interactions were formed with the active site and specificity sub-pockets of the meprin beta metalloprotease. This study assists in the development of the new lead molecules as well as gives a better understanding of their interaction with meprin-β.