噻唑类Fascin蛋白抑制剂的3D-QSAR研究

本文针对43个噻唑衍生物Fascin蛋白抑制剂,运用CoMFA(比较分子力场分析)以及CoMSIA(比较分子相似性指数分析)这两种经典的3D-QSAR方法,建立了CoMFA模型和CoMSIA模型,分别对其进行三维定量构效关系研究。CoMFA模型和CoMSIA模型的交叉验证系数q~2分别为0.731和0.846,相关系数r~2分别为0.969和0.926。这两种模型都显示出了比较好的预测性和稳定性。它们的三维等势图以及对接结果也证实了抑制剂活性和结构特征之间的关系,可以为今后设计研究新型Fascin抑制剂而提供了理论基础。     

黄酮类醛糖还原酶抑制剂的定量构效关系研究

醛糖还原酶能促使体内的葡萄糖转化为山梨醇,与糖尿病并发症的发生和恶化有密切关系.研究显示黄酮类化合物具有较好的醛糖还原酶抑制能力.本文使用三维定量构效关系研究方法,包括比较分子场分析法和比较分子相似性指数分析法,建立76个黄酮类醛糖还原酶抑制剂的分子结构与生物活性之间的定量关系模型,为进一步进行该类抑制剂的活性与三维结构关系的研究提供重要依据.采用PLS分析法,得到了一个统计意义显著的构效关系模型,其交叉验证系数为0.666,非交叉验证相关系数为0.918,显示该模型具有较好的预测能力.该模型使用立体场、静电场、疏水场和氢键受体场,可以较好地解释抑制剂的活性与其结构的关系。此外,本文还使用"留九法"、Y-randomization和外部检验法等检验模型的稳定性和预测能力.本研究结果可为设计和开发活性更高的该类抑制剂提供理论参考.     

2,5-二酮哌嗪类微管蛋白抑制剂的3D-QSAR研究

微管蛋白对细胞增殖极为重要,现已成为抗癌药物研发的重要靶标之一。针对53个以2,5-二酮哌嗪为基本骨架的微管蛋白抑制剂,分别运用比较分子力场分析(CoMFA)以及比较分子相似性指数分析(CoMSIA)2种经典方法进行了三维定量构效关系(3D-QSAR)研究,并依次建立了相关的模型。CoMFA模型的交叉验证系数q~2为0.642,相关系数r~2为0.996:CoMSIA模型的q~2和r~2,分别为0.725,0.908。模型具有较好的预测能力和较强的稳定性。3D-QSAR模型三维等势图揭示了一些结构特征与抑制活性的关系。我们希望这些研究为该类药物今后的设计和筛选提供可靠的理论依据。     

N-取代-4-取代苯基哌嗪-1-乙酰胺类α1-肾上腺素受体拮抗剂的三维定量构效关系研究

寻找α1-肾上腺素受体拮抗剂化学结构与生物活性之间的关系，为设计新的α1-受体拮抗剂提供理论依据，对28个N-取代-4-取代苯基哌嗪-1-乙酰胺类α1-受体拮抗剂，以自组织分子场分析法进行了三维定量构效关系研究。结果表明，最优SOMFA模型得到交叉验证相关系数q^2为0．733，回归系数r^2为0．740，建立的3D-QSAR模型应有一定的活性预测能力。     

基于Topomer CoMFA方法的苯基哌嗪类5-HT_7受体拮抗剂的3D-QSAR研究

5-HT_7受体是5-HT受体家族成员之一,主要参与体温、睡眠和情感性精神障碍的调节,5-HT_7受体拮抗剂已成为开发新型抗抑郁药物的一个重要思路。本文使用Sybyl-X2.0软件中的Topomer CoMFA方法对苯基哌嗪类5-HT_7受体拮抗剂进行三维定量构效关系分析。首先以苯基哌嗪作为母核,对化合物进行切割,得到4个R基片段,再通过自动叠合每个R基片段,分别计算所产生的静电场和立体场,最后得到了该类化合物作为5-HT_7受体拮抗剂的3D-QSAR模型。其交叉验证相关系数q~2为0.744,非交叉验证相关系数r~2为0.871,表明该模型稳定可靠,具有较好的预测能力。根据QSAR模型的结果在化合物29的基础上进行分子设计,得到了一些可能具有较高活性及成药性的化合物,该QSAR模型的研究结果可为新型5-HT_7受体拮抗剂的设计提供参考。     

DNA依赖蛋白激酶抑制剂的CoMFA研究

运用比较分子力场分析方法(CoMFA),以DNA依赖蛋白激酶(DNA-PK)抑制剂分子为研究对象,建立1组对DNA依赖蛋白激酶有抑制活性化合物的三维定量构效关系(3D-QSAR)模型,探索其活性数据和三维结构参数的关系,所建最佳模型交叉验证相关系数q2=0.670,非交叉验证相关系数R2=0.993,标准偏差SD=0.053,说明该模型预测能力较好.根据CoMFA模型的三维等势图可知,小体积、电负性大的取代基团,能提高该类化合物的活性,为新型DNA-PK抑制剂分子的设计提供了理论依据.     

NMDA受体非竞争性拮抗剂的3D-QSAR研究

NMDA受体(N-methyl-D-aspartate receptor)是离子型谷氨酸受体(ionotropic glutamate receptors,iGluRs)的一亚型,对谷氨酸的神经兴奋毒性起关键性作用,因此对于NMDA受体拮抗剂的应用已引起广泛重视。本研究选用NMDA受体甘氨酸位点拮抗剂1,4-二氢喹喔啉-2,3-二酮衍生物(QXs)为研究对象,采用比较分子场分析法(CoMFA)建立34个NMDA受体拮抗剂的三维定量构效关系(3D-QSAR)模型。此CoMA模型的交叉验证相关系数(q~2)0.566,最佳主成分数(ONC)6,非交叉验证相关系数(r~2)0.969,标准方差(SEE)0.236,立体场和静电场贡献值分别为62.3%和37.7%,研究结果可用分子场等势图直观表示。分子场等势图结果表明,在1,4-二氢喹喔啉-2,3-二酮衍生物苯环2,3位,减少取代基体积或增加取代基的正电性,可以提高该类化合物的活性。所建模型的预测能力和拟合能力较好,不仅了解清楚NMDA受体非竞争性拮抗剂的结构特征,还为设计活性更高的受体拮抗剂提供理论依据。     

比较分子力场法研究5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4-三唑[4,3-a]吡啶类抑制剂的定量构效关系

利用比较分子力场分析法(CoMFA),以5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4一三唑[4,3-a]吡啶类抑制剂为研究对象,建立一组对嗜酸性粒细胞磷酸二酯酶有抑制活性的化合物及其三维定量构效关系(3D-QSAR)模型,探索化合物活性数据和三维结构参数之间的关系.模型的交叉验证相关系数q2=0.565,非交叉验证相关系数r2=0.867,标准偏差SE=0.362,F=49.782,立体场和静电场的贡献值分别为72.7%和27.3%.该模型的预测能力较好,能够增大取代基体积和降低取代基电负性,可以提高该类化合物的活性.     

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。     

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。     

CoMFA analysis on a set of new noncompetitive GABAA receptor antagonists： fipronil and related analogs

Fipronil and related analogs, a set of new noncompetitive GABAA receptor antagonists, were investigated using comparative molecular field analysis （CoMFA） to explore their three-dimensional quantitative structure-activity relationships （3D-QSAR）. Considering the structural complexity of molecules of fipronil and related analogs, three different alignments were performed in this paper. CoMFA model for housefly receptor yield the leave-one-out and cross-validated correlation coefficient q^2 value of 0.511 and the conventional correlation coefficient r^2 value of 0.997. The new compounds with higher activity would be designed from this model. CoMFA model for rat receptor was not successful using all these three alignments, the reason of which maybe that some molecules adopt different conformations for rat receptor.     

两类促肾上腺皮质释放因子(CRF)抑制剂的CoMFA研究

Fipronil and related analogs, a set of new noncompetitive GABAA receptor antagonists, were investigated using comparative molecular field analysis (CoMFA) to explore their three-dimensional quantitative structure-activity relationships (3D-QSAR).Considering the structural complexity of molecules of fipronil and related analogs, three different alignments were performed in this paper. CoMFA model for housefly receptor yield the leave-one-out and cross-validated correlation coefficient q2 value of 0.511 and the conventional correlation coefficient r2 value of 0.997. The new compounds with higher activity would be designed from this model.CoMFA model for rat receptor was not successful using all these three alignments, the reason of which maybe that some molecules adopt different conformations for rat receptor.     

Three-dimensional molecular field analyses of octopaminergic agonists and antagonists for the locust neuronal octopamine receptor class 3

The quantitative structure-activity relationship (QSAR) of a set of 70 octopaminergic agonists and 20 antagonists against octopamine receptor class 3 (OAR3) in locust nervous tissue was analyzed by molecular field analysis (MFA). MFA of these compounds evaluated effectively the energy between a probe and a molecular model at a series of points defined by a rectangular grid. Contour surfaces for the molecular fields are presented. These results provide useful information in the characterization and differentiation of octopaminergic receptor types and subtypes.     

A3 adenosine receptor: Homology modeling and 3D-QSAR studies

Adenosine receptors (AR) belong to the superfamily of G-protein-coupled receptors (GPCRs). They are divided into four subtypes (A1, A2A, A2B, and A3) and can be distinguished on the basis of their distinct molecular structures, distinct tissues distribution, and selectivity for adenosine analogs. The hA3R, the most recently identified adenosine receptor, is involved in a variety of intracellular signaling pathways and physiological functions. Expression of hA3R was reported to be elevated in cancerous tissues and A3 antagonists could be proposed for therapeutic treatments of tumor. By using the crystal structure of hA2A adenosine receptor, recently published, we were able to obtain a model for A3R, further optimized using nanosecond scale molecular dynamics simulation. One hundred twenty two active and selective compounds were docked into this model and used as training set to generate pharmacophore models. These last address the prevalent features to be used for the search of new inhibitors. Therefore, it was employed as template to screen the ZINC database in the attempt to find new potent and selective human A3R antagonists. Our theoretical model of hA3 adenosine receptor was used to evaluate and quantify the structure-activity relationship of known antagonists. Moreover the obtained 3D-QSAR model allowed to identify new potential inhibitors.     

New insights into the stereochemical requirements of the bradykinin B1 receptor antagonists binding

Bradykinin (BK) is a nonapeptide involved in several pathophysiological conditions including among others, septic and haemorrhagic shock, anaphylaxis, arthritis, rhinitis, asthma, inflammatory bowel disease. Accordingly, BK antagonists have long been sought after for therapeutic intervention. Action of BK is mediated through two different G-protein coupled receptors known as B1 and B2. Although there are several B1 antagonists reported in literature, their pharmacological profile is not yet optimal so that new molecules need to be discovered. In the present work we have constructed an atomistic model of the B1 receptor and docked diverse available non-peptide antagonists in order to get a deeper insight into the structure-activity relationships involving binding to this receptor. The model was constructed by homology modeling using the chemokine CXC4 and bovine rhodopsin receptors as template. The model was further refined using molecular dynamics for 600 ns with the protein embedded in a POPC bilayer. From the refinement process we obtained an average structure that was used for docking studies using the Glide software. Antagonists selected for the docking studies include Compound 11, Compound 12, Chroman28, SSR240612, NPV-SAA164 and PS020990. The results of the docking study underline the role of specific receptor residues in ligand binding. The results of this study permitted to define a pharmacophore that describes the stereochemical requirements of antagonist binding, and can be used for the discovery of new compounds.     

3D QSAR studies of 1,3,4-benzotriazepine derivatives as CCK2 receptor antagonists

A number of CCK(2) antagonists have been reported to play an important role in controlling gastric acid-related conditions, nervous system related disorders and certain types of cancer. To obtain the helpful information for designing potent antagonists with novel structures and to investigate the quantitative structure-activity relationship of a group of 62 different CCK(2) receptor antagonists with varying structures and potencies, CoMFA, CoMSIA, and HQSAR studies were carried out on a series of 1,3,4-benzotriazepine-based CCK(2) receptor antagonists. QSAR models were derived from a training set of 49 compounds. By applying leave-one-out (LOO) cross-validation study, cross-validated (r(cv)(2)) values of 0.673 and 0.608 and non-cross-validated (r(ncv)(2)) values of 0.966 and 0.969 were obtained for the CoMFA and CoMSIA models, respectively. The predictive ability of the CoMFA and CoMSIA models was determined using a test set of 13 compounds, which gave predictive correlation coefficients (r(pred)(2)) of 0.793 and 0.786, respectively. HQSAR was also carried out as a complementary study, and the best HQSAR model was generated using atoms, bonds, hydrogen atoms, and chirality as fragment distinction with fragment size (2-5) and six components showing r(cv)(2) and r(ncv)(2) values of 0.744 and 0.918, respectively. CoMFA steric and electrostatic, CoMSIA hydrophobic and hydrogen bond acceptor fields, and HQSAR atomic contribution maps were used to analyze the structural features of the datasets that govern their antagonistic potency.