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.     

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。     

基于药效团的BRD4靶向小分子抑制剂虚拟筛选

BRD4靶点和多种肿瘤密切相关,是具有良好成药性的热门靶点。本文选取活性较好且结构差异较大的BRD4小分子抑制剂作为训练集分子,基于配体小分子共同特征(HipHop)方法使用Discovery Studio 3.0分子模拟软件构建了药效团。药效团通过测试集验证、ROC曲线验证(SE(sensitivity)=0.93765、SP(specificity)=0.89500、(AUC)=0.956),结果表明构建得到的药效团具有较强的可靠性和较高的可信度。药效团模型含有1个芳环中心、1个疏水基团、2个氢键受体四个药效特征元素。此药效团被用于ZINC数据库进行虚拟筛选,共筛选了861203个分子,命中率为0.782%。再对筛选得到的分子经过分子对接、ADMET成药性预测、构象分析并讨论分子-蛋白相互作用模式,最终得到了21个有潜力的BRD4小分子抑制剂。     

2,4-二氨基嘧啶类黏着斑激酶抑制剂的3D-QSAR研究

本文选取42个2,4-二氨基嘧啶类黏着斑激酶(FAK)小分子抑制剂,分别以比较分子场分析法(CoMFA)与相似性指数分析法(CoMSIA)构建3D-QSAR模型,评价模板分子、公共骨架点、最小能量优化参数、分子构象等因素对模型优化的影响。分析最优模型中立体场、静电场以及氢键等因素对抑制剂活性的影响,并应用分子对接分析该类抑制剂与FAK蛋白的相互作用。结果表明,选择化合物16作为模板分子,骨架A作为公共骨架点,最小能量优化参数中电荷、最大迭代系数、最低能量限定值分别为MMFF94、1000、0.01kcal/mol时所构建的模型最优。以CoMFA和CoMSIA构建的3D-QSAR模型的交叉验证系数(q~2)分别为0.666和0.736,非交叉验证系数(R~2)分别为0.990和0.989,表明此模型具有良好的预测能力。分子对接分析显示,其与FAK的氨基酸残基CYS502、ASP564形成了重要的氢键作用,并与周围残基形成了较强的疏水作用。通过3D-QSAR的构建与分子对接分析,可指导2,4-二氨基嘧啶类FAK小分子抑制剂的进一步结构优化设计。     

光系统Ⅱ抑制剂—反式氰基丙烯酸酯比较分子场和电子结构研究

对反式氰基丙烯酸酯系列活性分子采用限制性系统搜索方法确定的药效团模型 ,与 9类不同骨架结构的光系统 抑制剂 DISCO模型中的反式氰基丙烯酸酯分子(M- 2 2 )的活性构象为模板所确定的药效团模型是非常相近的。对两种方法所获得的活性构象分子进行了 Co MFA研究 ,其结果是一致的。采用 PM3方法进行了量子化学计算 ,计算结果表明两种模型的构象分子具有相近的电子结构 ,根据分子静电场、立体场和电子结构探讨了该类抑制剂的构效关系。     

二芳基三嗪类HIV-1逆转录酶抑制剂的分子对接及3D-QSAR研究

对40个二芳基三嗪类HIV-1逆转录酶抑制剂进行了分子对接研究,结果表明,2个疏水性芳香取代基团与结合口袋底部形成的疏水和范德华相互作用、三嗪环母核及其R4取代基与结合位点产生的氢键和静电作用以及R4取代基与袋口形成的空间位阻效应是影响该系列化合物活性的重要因素.根据对接优势构象进行分子叠合和比较分子相似性指数分析(C...     

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.     

基于药效团的血管紧张素转换酶抑制肽的结构优化

采用Discovery Studio2．0中的药效团模型生成方法,产生了基于化学特征的ACE抑制肽的药效团模型．所选择的认为最好的药效团模型（Hypo1）含有5个化学特征（1个阴离子中心、1个氢键受体、1个氢键给体、2个疏水中心）．我们先前采用实验的方法,从蚕蛹蛋白中获得具有ACE抑制活性的六肽分子,本文结合产生的ACE抑制肽药效团模型和分子对接研究,对该六肽分子进行结构优化,以识别六肽中对ACE抑制活性起关键作用的结构部分．结果显示,药效团模型的方法可有效用于ACE抑制肽的结构优化．     

黄嘌呤型cPEPCK抑制剂的计算模型建立

通过SYBYL X 2.0软件包中Topomer CoMFA和Surflex-dock研究28种黄嘌呤结构的胞浆型磷酸烯醇式丙酮酸羧激酶(cPEPCK)抑制剂的三维定量构效关系(3D-QSAR)和分子对接模式.所得优化的3D-QSAR模型q2和r~2分别为0.907和0.994(q~20.5时,建立的模型具有可靠的预测能力),结果表明该模型具有较高预测能力.分子对接结果显示,此类分子与cPEPCK的Asn 533,Asn 292和Phe 530等活性功能残基具有氢键作用.Trp516可能是潜在的活性残基.基于以上研究讨论,为设计和开发具有较高活性的新型黄嘌呤类cPEPCK抑制剂提供有效信息.     

中药活性成分对血栓素A2受体抑制作用的分子模拟

中药中黄酮类化合物和白藜芦醇等活性成分对血栓素A2受体具有抑制作用,但具体机理不详.本研究通过同源模建方法,以墨鱼视紫红质蛋白为模板,构建血栓素A2受体的蛋白质结构模型.并使用分子对接方法研究中药活性成分白藜芦醇和芹菜苷元与血栓素A2受体模型的作用方式,据此建立药效团模型,筛选其他潜在的血栓素A2受体抑制剂.结果表明:白藜芦醇等中药活性成分能与血栓素A2受体活性口袋中的残基发生氢键作用,结合方式与血栓素相似.血栓素与Ser201、Leu198、Arg295和Thr298发生氢键作用,白藜芦醇等活性成分与Ser201、Leu198和Arg295发生氢键作用.建立的药效团模型由7个药效元素以及排斥性空间元素组成,经测试对高活性的血栓素A2受体抑制剂有比较好的选择性.使用该药效团模型对中药天然产物数据库进行筛选,命中了一批可能具有血栓素A2受体抑制作用的活性化合物.其中一些已经报道有抑制血小板凝聚活性.本研究表明血栓素A2受体可能是活血化瘀类中药的一个潜在的靶点.     

Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies

HIV-1 integrase (IN) is a retroviral enzyme that catalyses integration of the reverse-transcribed viral DNA into the host genome, which is necessary for efficient viral replication. In this study, we have performed an in silico virtual screening for the identification of potential HIV-1 IN strand transfer (ST) inhibitors. Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to 3-Hydroxypyrimidine-2,4-diones. Based on the ligand-based pharmacophore model, we obtained a five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features. The pharmacophore hypothesis AADHR was used as a 3D query in a sequential virtual screening study to filter small molecule databases Maybridge, ChemBridge and Asinex. Hits matching with pharmacophore hypothesis AADHR were retrieved and passed progressively through Lipinski’s rule of five filtering, molecular docking and hierarchical clustering. The five compounds with best hits with novel and diverse chemotypes were subjected to QM/MM docking, which showed improved docking accuracy. We further performed molecular dynamics simulation and found three compounds that form stable interactions with key residues. These compounds could be used as a leads for further drug development and rational design of HIV-1 IN inhibitors.     

Homology modeling,docking and structure-based pharmacophore of inhibitors of DNA methyltransferase

DNA methyltransferase 1 (DNMT1) is an emerging epigenetic target for the treatment of cancer and other diseases. To date, several inhibitors from different structural classes have been published. In this work, we report a comprehensive molecular modeling study of 14 established DNTM1 inhibitors with a herein developed homology model of the catalytic domain of human DNTM1. The geometry of the homology model was in agreement with the proposed mechanism of DNA methylation. Docking results revealed that all inhibitors studied in this work have hydrogen bond interactions with a glutamic acid and arginine residues that play a central role in the mechanism of cytosine DNA methylation. The binding models of compounds such as curcumin and parthenolide suggest that these natural products are covalent blockers of the catalytic site. A pharmacophore model was also developed for all DNMT1 inhibitors considered in this work using the most favorable binding conformations and energetic terms of the docked poses. To the best of our knowledge, this is the first pharmacophore model proposed for compounds with inhibitory activity of DNMT1. The results presented in this work represent a conceptual advance for understanding the protein–ligand interactions and mechanism of action of DNMT1 inhibitors. The insights obtained in this work can be used for the structure-based design and virtual screening for novel inhibitors targeting DNMT1.     

Pharmacophore generation,atom-based 3D-QSAR and molecular dynamics simulation analyses of pyridine-3-carboxamide-6-yl-urea analogues as potential gyrase B inhibitors

DNA gyrase subunit B (GyrB) is an attractive drug target for the development of antibacterial agents with therapeutic potential. In the present study, computational studies based on pharmacophore modelling, atom-based QSAR, molecular docking, free binding energy calculation and dynamics simulation were performed on a series of pyridine-3-carboxamide-6-yl-urea derivatives. A pharmacophore model using 49 molecules revealed structural and chemical features necessary for these molecules to inhibit GyrB. The best fitted model AADDR.13 was generated with a coefficient of determination (r²) of 0.918. This model was validated using test set molecules and had a good r² of 0.78. 3D contour maps generated by the 3D atom-based QSAR revealed the key structural features responsible for the GyrB inhibitory activity. Extra precision molecular docking showed hydrogen bond interactions with key amino acid residues of ATP-binding pocket, important for inhibitor binding. Further, binding free energy was calculated by the MM-GBSA rescoring approach to validate the binding affinity. A 10 ns MD simulation of inhibitor #47 showed the stability of the predicted binding conformations. We identified 10 virtual hits by in silico high-throughput screening. A few new molecules were also designed as potent GyrB inhibitors. The information obtained from these methodologies may be helpful to design novel inhibitors of GyrB.     

Enhancing specificity and sensitivity of pharmacophore-based virtual screening by incorporating chemical and shape features--a case study of HIV protease inhibitors

Virtual screening (VS), if applied appropriately, could significantly shorten the hit identification and hit-to-lead processes in drug discovery. Recently, the version of VS that is based upon similarity to a pharmacophore has received increased attention. This is due to two major factors: first, the public availability of the ZINC1 conformational database has provided a large selection pool with high-quality and purchasable small molecules; second, new technology has enabled a more accurate and flexible definition of pharmacophore models coupled with an efficient search speed. The major goal of this study was to achieve improved specificity and sensitivity of pharmacophore-based VS by optimizing the variables used to generate conformations of small molecules and those used to construct pharmacophore models from known inhibitors or from inhibitor-protein complex structures. By using human immunodeficiency virus protease and its inhibitors (PIs) as a case study, the impact of the key variables, including the selection of chemical features, involvement of excluded volumes (EV), the tolerance radius of excluded volumes, energy windows, and the maximum number of conformers in conformation generation, was explored. Protein flexibility was simulated by adjusting the sizes of EV. Our best pharmacophore model, combining both chemical features and excluded volumes, was able to correctly identify 60 out of 75 structurally diverse known PIs, while misclassifying only 5 out of 75 similar compounds that are not inhibitors. To evaluate the specificity of the model, 1193 oral drugs on the market were screened, and 25 original hits were identified, including 5 out of 6 known PI drugs.     

Pharmacophore and Docking-based 3D-QSAR Studies on HIV-1 Integrase Inhibitors

Integrase（IN） plays an essential role in the process of HIV-1 replication.IN inhibitors of diketo acid derivatives（DKAs） were analysed by the Comparative Molecular Field Analysis（CoMFA） and Comparative Molecular Similarity Induces Analysis（CoMSIA） methods.A set of 42 compounds were randomly selected as the training set（35） and test set（7）.Firstly,a good pharmacophore（goodness of hit=0.787） was obtained and used to align ligands.Then,predictive models were constructed with the CoMFA and CoMSIA methods based on the pharmacophore alignment.As a result,the CoMS1A method yielded the best model with an r2 of 0.955 and a q2 of 0.665,which can predict the activities of the tested DKAs very well（r2=0.559）.Finally,DKAs were docked into IN,and the predicit modes were superimposed on the contour maps obtained from the best CoMSIA model.The superimposed maps gave a visualized and meaningful insight into the inhibitory behaviors,providing significantly useful information for the rational drug design of anti-IN agents.     

黄酮类醛糖还原酶抑制剂的抑制机理研究

为了研究黄酮类醛糖还原酶抑制剂的抑制机理, 选择了31个黄酮类化合物作为训练集, 使用Catalyst软件包构建了此类抑制剂的药效团模型. 并专门针对黄酮类化合物定制了氢键给体和受体模型, 效果优于使用Catalyst内预定义的模型. 最终的药效团模型由两个氢键给体和一个氢键受体组成, 对训练集具有较好预测能力(Correl=0.9013). 此外, 使用InsightII/Affinity对6个黄酮类化合物进行了分子对接研究. 综合药效团模型和分子对接研究的结果, 发现黄酮类化合物的抑制活性主要源于黄酮骨架上的C4’或C3’位的羟基与醛糖还原酶活性口袋中的TYR48、VAL47、GLN49和C7位的羟基与HIS110, TRP111所形成的两组氢键.     

A pseudo-ligand approach to virtual screening

A virtual screening method is presented that is grounded on a receptor-derived pharmacophore model termed "virtual ligand" or "pseudo-ligand". The model represents an idealized constellation of potential ligand sites that interact with residues of the binding pocket. For rapid virtual screening of compound libraries the potential pharmacophore points of the virtual ligand are encoded as an alignment-free correlation vector, avoiding spatial alignment of pharmacophore features between the pharmacophore query (i.e., the virtual ligand) and the candidate molecule. The method was successfully applied to retrieving factor Xa inhibitors from a Ugi three-component combinatorial library, and yielded high enrichment of actives in a retrospective search for cyclooxygenase-2 (COX-2) inhibitors. The approach provides a concept for "de-orphanizing" potential drug targets and identifying ligands for hitherto unexplored or allosteric binding pockets.     

A virtual screening approach for thymidine monophosphate kinase inhibitors as antitubercular agents based on docking and pharmacophore models

Docking and pharmacophore screening tools were used to examine the binding of ligands in the active site of thymidine monophosphate kinase of Mycobacterium tuberculosis. Docking analysis of deoxythymidine monophosphate (dTMP) analogues suggests the role of hydrogen bonding and other weak interactions in enzyme selectivity. Water-mediated hydrogen-bond networks and a halogen-bond interaction seem to stabilize the molecular recognition. A pharmacophore model was developed using 20 dTMP analogues. The pharmacophoric features were complementary to the active site residues involved in the ligand recognition. On the basis of these studies, a composite screening model that combines the features from both the docking analysis and the pharmacophore model was developed. The composite model was validated by screening a database spiked with 47 known inhibitors. The model picked up 42 of these, giving an enrichment factor of 17. The validated model was used to successfully screen an in-house database of about 500,000 compounds. Subsequent screening with other filters gave 186 hit molecules.     

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.