Homology model-guided 3D-QSAR studies of HIV-1 integrase inhibitors

In the present study, we report the exploration of binding modes of potent HIV-1 integrase (IN) inhibitors MK-0518 (raltegravir) and GS-9137 (elvitegravir) as well as chalcone and related amide IN inhibitors we recently synthesized and the development of 3D-QSAR models for integrase inhibition. Homology models of DNA-bound HIV-1 IN were constructed on the basis of the X-ray crystal structure of the foamy virus IN-DNA complex (PDB ID: 3L2T ) and used for docking. The binding modes of raltegravir and elvitegravir in our homology models are in accordance with those in the foamy virus structure revealing interactions important for inhibitor-IN binding. To gain further insights into the structural requirements for IN inhibition, three-dimensional quantitative structure activity relationship (3D-QSAR) studies were conducted using raltegravir, elvitegravir, and their analogs; our synthesized 3-keto salicylic acid IN inhibitor series; as well as other structurally related HIV-1 IN inhibitors. In the first part of the study with 103 compounds, atom-fit alignments, I and II, and docking-based alignment, III, were used to develop 3D-QSAR models 1, 2, and 3, respectively, each comprising comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSARs. This initial analysis indicated that the docking-based (structure-based) model 3 performed better than the atom-fit (ligand-based) models 1 and 2, in terms of statistical significance and robustness. Thus, the docking-based alignment was then subsequently used with an expanded data set of 296 compounds for building a more comprehensive 3D-QSAR, model 4. Model 4 afforded good q2 values of 0.70 and 0.75 for CoMFA and CoMSIA 3D-QSARs, respectively, and showed good predictive performance on an external validation test set of 59 compounds with predictive r2 values up to 0.71. The HIV IN-DNA homology model of biological relevance and the comprehensive 3D-QSAR models developed in the present study provide insights and new predictive tools for structure-based design and optimization of IN inhibitors.     

Comparison of 3D quantitative structure-activity relationship methods: Analysis of the in vitro antimalarial activity of 154 artemisinin analogues by hypothetical active-site lattice and comparative molecular field analysis

Two three-dimensional quantitative structure-activity relationship (3D-QSAR) methods, comparative molecular field analysis (CoMFA) and hypothetical active site lattice (HASL), were compared with respect to the analysis of a training set of 154 artemisinin analogues. Five models were created, including a complete HASL and two trimmed versions, as well as two CoMFA models (leave-one-out standard CoMFA and the guided-region selection protocol). Similar r2 and q2 values were obtained by each method, although some striking differences existed between CoMFA contour maps and the HASL output. Each of the four predictive models exhibited a similar ability to predict the activity of a test set of 23 artemisinin analogues, although some differences were noted as to which compounds were described well by either model.     

Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q(2) values of 0.701 and 0.627 and the r(2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.     

马来酰胺类糖原合成酶激酶-3β抑制剂的分子对接和三维定量构效关系

通过分子对接和三维定量构效关系(3D-QSAR)两种方法来确定两类马来酰胺类的糖原合成酶激酶-3β(GSK-3β)抑制剂的结合方式. 首先, 用分子对接确定抑制剂与GSK-3β结合模式及其相互作用; 然后用比较分子力场分析法(CoMFA)与比较分子相似性指数分析法(CoMSIA)对48个化合物做三维定量构效关系的分析. 两种方法得出的交互验证回归系数分别为0.669(CoMFA)和0.683(CoMSIA), 证明该模型具有很好的统计相关性, 同时也说明该模型具有较高的预测能力.根据该模型提供的信息, 设计出9个预测活性较好的分子.     

3D-QSAR Studies on a Series of Indoleamide Derivatives as Antiplasmodial Drugs

In this study, three-dimensional quantitative structure-activity relationship(3D-QSAR) was studied for the antiplasmodial activity of a series of novel indoleamide derivatives by comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(Co MSIA). 3D-QSAR model was established by a training set of 20 compounds and was externally validated by a test set of 4 compounds. The best prediction(Q~2 = 0.593 and 0.527, R~2 = 0.990 and 0.953, r_(pred)~2 = 0.967 and 0.962 for CoMFA and CoMSIA) was obtained according to CoMFA and CoMSIA. Those parameters indicated the model was reliable and predictable. We designed several molecules with high activities according to the contour maps produced by the CoMFA and CoMSIA models.     

马来酰胺类糖原合成酶激酶-3β抑制剂的分子对接和三维定量构效关系

通过分子对接和三维定量构效关系(3D-QSAR)两种方法来确定两类马来酰胺类的糖原合成酶激酶-3β(GSK-3β)抑制剂的结合方式.首先,用分子对接确定抑制剂与GSK-3β的结合模式及其相互作用;然后用比较分子力场分析法(CoMFA)与比较分子相似性指数分析法(CoMSIA)对48个化合物做三维定量构效关系的分析.两种方法得出的交互验证回归系数分别为0.669(CoMFA)和0.683(CoMSIA),证明该模型具有很好的统计相关性,同时也说明该模型具有较高的预测能力.根据该模型提供的信息,设计出9个预测性较好的分子.     

CBP/P300溴结构域联芳基类抑制剂三维定量构效关系研究

CREB结合蛋白(CBP)和与其高度同源的P300蛋白是组蛋白乙酰化酶的两个亚型,两者通过它们的溴结构域(bromodomain,BRD)与染色质结合,目前,CBP/P300已经成为人类在肿瘤靶点领域中的研究热点。本研究基于CBP/P300溴结构域联芳基类抑制剂建立三维定量构效关系,采用比较分子力场分析法(Co MFA)和比较分子相似性指数分析法(Co MSIA)分别建立35个已知活性抑制剂的3D-QSAR模型,以确定CBP/P300溴结构域联芳基类抑制剂分子结构与生物活性之间的定量关系。Co MFA和Co MSIA模型活性数据p IC50的预测值与实验值基本一致,说明这两个模型具有较高的预测能力和统计学意义。根据Co MFA和Co MSIA模型所提供的立体场、静电场、疏水场、氢键给体场、氢键供体场等信息提出了改善此类抑制剂活性的药物设计思路,为指导设计具有更高活性的新分子和预测更加有效的CBP/P300溴结构域抑制剂提供理论依据。     

2D/3D-QSAR Studies of [1,2,4]Triazolo[1,5-a]pridinylpyridine Derivatives as Potent Anticancer Agents

By using a combined method of density functional theory(DFT), molecular mechanics(MM2) and statistics for two-dimensional(2D), as well as the comparative molecular field analysis(Co MFA) and comparative molecular similarity index analysis(Co MSIA) methods for three-dimensional(3D), theoretical studies on 2D/3D quantitative structure-activity relationships(QSAR) of 22 novel compounds of [1,2,4]triazolo[1,5-a] pyridinylpyridines acting as PI3 K inhibitors against the human colon carcinoma cell line(HCT-116) have been performed. Both the 2D- and 3D-QSAR models established from the random 18 compounds in training set show significant statistical quality and satisfactory predictive ability(R2 = 0.821, q2 = 0.773 for 2D-QSAR, R2 = 0.966, q2 = 0.668 for Co MFA, R2 = 0.979, q2 = 0.753 for Co MSIA). The combined 2D- and 3D-QSAR studies suggest that the moderate-size, hydrophilic and electron-withdrawing group at R1 position, the bulky and hydrophobic group at R2 position, and the minor, hydrophobic, H-bond donor and electron-donating group at R3 position would enhance the anticancer activities. These obtained results help to insight into the action mechanism, and will serve as a basis for the design of new potent anticancer agents.     

N-取代马来酰亚胺类hMGL抑制剂的3D-QSAR、分子对接和分子动力学研究

本文通过三维定量构效关系(3D-QSAR)建模、分子对接和分子动力学模拟,探讨了41个N-取代马来酰亚胺类衍生物与人单酰甘油脂肪酶(hMGL)的相互作用,并构建了相关模型。其中,比较分子力场分析模型(CoMFA、q₂ ^{= 0. 5}41、r₂ ^{= 0. 9}72)和比较分子相似性指数分析模型(CoMSIA、q₂ ^{= 0. 5}88、r₂ ^{= 0. 9}19)具有较好的预测能力。QSAR模型等势图阐明了该系列化合物生物活性与结构的关系,并依此设计了系列衍生物。采用分子对接和分子动力学模拟探讨了高活性化合物36、46与hMGL(PDB ID: 3PE6)的结合模式和稳定性,结果表明二者主要通过氢键和疏水相互作用与hMGL结合并且形成了稳定的复合物。随后对pIC₅₀预测值优于文献报道中最高活性化合物36的8个衍生物进行分子对接和ADMET预测,选择2个衍生物进行分子动力学模拟,结果表明2个衍生物分别与hMGL形成的复合物结合构象稳定。本研究为新型N-取代马来酰亚胺类单酰甘油脂肪酶抑制剂的开发提供了理论依据。     

3-氨磺酰苯甲酸类AKR1C3抑制剂的3D-QSAR和分子对接研究

醛酮还原酶1C3(AKR1C3)作为治疗前列腺癌的新靶点已成为研究热点,3-氨磺酰苯甲酸衍生物对其具有高效的选择性和抑制活性。本文采用比较分子场分析(COMFA)和比较分子相似性指数分析(COMSIA)方法,将经分子对接后的34个优势构象组成训练集和11个优势构象组成测试集,构建三维定量构效关系(3D-QSAR)模型。COMFA模型的交叉验证系数(q2),非交叉验证系数(R2),标准偏差(SEE)和F值分别为0.761,0.973,0.122,185.963;自举法回归系数为R2bs=0.98。最佳组合COMSIA模型的q2,R2,SEE,F和R2bs分别为0.734,0.984,0.097,147.850,0.994。COMFA和COMSIA模型的系统外部测试R2pred分别为0.864和0.756,r2m分别为0.8127和0.5377。这些结果表明,所建立的QSAR模型具有较高的可靠性和较强预测能力。经三维等势图分析可知,在2、5或6位适当增加取代基体积,或在5位引入氢键受体,或在7位引入负电性取代基则能提高化合物的生物活性。该模型为进一步设计具有更优选择性和活性的化合物提供了理论依据。     

楝酰胺(Rocaglamide)类化合物的三维定量构效关系

采用比较分子力场分析(CoMFA)和比较分子相似因子分析(CoMSIA)方法,对训练集中的26个楝酰胺(Rocaglamide)类化合物进行了三维定量构效关系(3D-QSAR)研究,最终建立的CoMFA模型和CoMSlA模型的q<'2>分别为0.593和0.656.并对测试集中的5个化合物的生物活性进行了预测,结果表明...     

A selectivity study of benzenesulfonamide derivatives on human carbonic anhydrase II/IX by 3D-QSAR,Molecular Docking and Molecular Dynamics Simulation

Nowadays, different approaches have been pursued with the intent to develop sulfonamide-like carbonic anhydrase inhibitors that possess better selectivity profiles toward the different human isoforms of the enzyme. Here, we used conventional 3D-QSAR methods, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA, to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models for benzenesulfonamide derivatives as human carbonic anhydrase (hCA) II/IX inhibitors. The theoretical models had good reliability (R²>0.75) and predictability (Q²>0.55), and the contour maps could graphically present the contributions of the force fields for activity and identify the structural divergence between human carbonic anhydrase II inhibitors and human carbonic anhydrase IX inhibitors. Consequently, we explored the selectivity of inhibitor for human carbonic anhydrase II and IX through molecular docking, and the difference of activity coincides with the potential binding mode well. According to the results of the predicted values and the molecule docking, we found that the inhibitors published in the literature had stronger inhibition on the hCA IX; based on the theoretical models, we designed seven new compounds with good potential activity and reasonably good ADMET profile, which could selectively inhibit hCA IX. Molecular Dynamics Simulation showed that newly-designed compound D7 had good selectivity on hCA IX. The findings from 3D-QSAR and docking studies maybe helpful in the rational drug design of isoform-selective inhibitors.     

Three-dimensional quantitative structure-activity relationship analysis of the new potent sulfonylureas using comparative molecular similarity indices analysis

The present study describes the implementation of a new three-dimensional quantitative structure-activity relationship (3D-QSAR) technique: comparative molecular similarity indices analysis (CoMSIA) to a set of novel herbicidal sulfonylureas targeted acetolactate synthase. Field expressions in terms of similarity indices in CoMSIA were applied instead of the usually used Lennard-Jones and Coulomb-type potentials in CoMFA. Two different kinds of alignment techniques including field-fit alignment and atom-by-atom fits were used to produce the molecular aggregate. The results indicated that those two alignment rules generated comparative 3D-QSAR models with similar statistical significance. However, from the predictive ability of the test set, the models from the alignment after maximal steric and electrostatic optimization were slightly better than those from the simple atom-by-atom fits. Moreover, systematic variations of some parameters in CoMSIA were performed to search the best 3D-QSAR model. A significant cross-validated q2 was obtained, indicating the predictive potential of the model for the untested compounds; meanwhile the predicted biological activities of the five compounds in the test set were in good agreement with the experimental values. The CoMSIA coefficient contour plots identified several key features explaining the wide range of activities, which were very valuable for us in tracing the properties that really matter and getting insight into the potential mechanisms of the intermolecular interactions between inhibitor and receptor, especially with respect to the design of new compounds.     

QSAR studies on hepatitis C virus NS5A protein tetracyclic inhibitors in wild type and mutants by CoMFA and CoMSIA

ABSTRACT

Several 3D-QSAR models were built based on 196 hepatitis C virus (HCV) NS5A protein inhibitors. The bioactivity values EC₉₀ for three types of inhibitors, the wild type (GT1a) and two mutants (GT1a Y93H and GT1a L31V), were collected to build three datasets. The programs OMEGA and ROCS were used for generating conformations and aligning molecules of the dataset, respectively. Each dataset was randomly divided into a training set and a test set three times to reduce the contingency of only one random selection. QSAR models were computed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the datasets GT1a, GT1a Y93H, and GT1a L31V, the best models CoMFA-INDX, CoMSIA-SEHA, and CoMSIA-SEHA showed an r² value of 0.682 ± 0.033, 0.779 ± 0.036, and 0.782 ± 0.022 on the test sets, respectively. From the contour maps of the three best models, we summarized the favourable and unfavourable substituents on the tetracyclic core, the Z group, the proline group, and the valine group of inhibitors. We guessed the mutants could change the electrostatic surfaces of the wild type active pocket. In addition, we used ECFP analyses to find important substructures and could intuitively understand the results from QSAR models.     

DATA类逆转录酶抑制剂的三维定量构效关系

采用对接方法得到HIV-1抑制剂DATA(二芳基三嗪类)分子的活性构象, 进一步用比较分子场分析(CoMFA)和比较分子相似性分析(CoMSIA)法对DATA类逆转录酶抑制剂(RTIs)的三维定量构效关系(3D-QSAR)进行了研究, 建立3D-QSAR模型, 以指导进一步结构修饰. 用此模型预测了5个DATA类似物, 预测偏差较小, 表明了所建立的模型具有较强的预测能力.     

Exploring the molecular basis for selective cytotoxicity of lamellarins against human hormone-dependent T47D and hormone-independent MDA-MB-231 breast cancer cells

Abstract  

The common structural requirements for cytotoxicity of lamellarins against two human breast cancer cell lines were determined using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Twenty lamellarins were selected to serve as the training set, whereas another group of six compounds were used as the test set. The best CoMFA and CoMSIA models for both cell lines yielded satisfactory predictive ability with r _cv² values in the range of 0.659–0.728. Additionally, the contour maps obtained from both the CoMFA and CoMSIA models agreed well with the experimental results and may be used in the design of more potent cytotoxic compounds for human breast cancers. Both analyses not only suggested structural requirements of various substituents around the lamellarin skeleton for their cytotoxic activity against both human breast cancer cell lines but also revealed the molecular basis for the differences between the saturated and unsaturated D-rings of the lamellarins.     

3D-QSAR Analysis of a Series of 1,2,3-Triazole-chromenone Derivatives as an Acetylcholinesterase Inhibitor against Alzheimer's Disease

Chromenones have attracted much attention since they are excellent acetylcholinesterase inhibitor(AChEi). The 1,2,3-triazoles are multifunctional anti-acetylcholinesterase(AChE) agents. In this paper, we report the three-dimensional quantitative structure-activity relationship(3D-QSAR) study of 25 1,2,3-triazolechromenone derivatives based comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA). To construct CoMFA and CoMSIA models, the 25 active molecules were randomly divided into the training and test sets. The obtained cross-validation Q~2 of the CoMFA model, the coefficient of non-cross-validation R~2, and the test value F are 0.597, 0.994, and 396.726, respectively. The cross-validation Q~2 of the CoMSIA model, the coefficient of the non-cross-validation R~2, and the test value F are 0.721, 0.979, and 131.107, respectively. The predictive correlation coefficient(r_(pred)~2) is 0.728 for CoMFA and 0.805 for CoMSIA, which verifies that the model is predictable. Based on the potential maps of CoMFA and CoMSIA, a library containing a set of potent AChEi was designed. The inhibitory potential of the compounds in this library was found to be greater than the inhibitory potential of the most active compounds in the data set. The results obtained from this study laid the foundation for the development of effective drugs for AChEi.