毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟,将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接,形成非特异性激动和特异性激动的受体-配体复合物.用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰且H碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns.将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分,以top5%富集因子(EF)作为评价依据,用占诺美林优化后的M1受体模型的EF为8.0,用乙酰胆碱优化后M1受体模型的EF为6.5,非复合物的EF为1.5.说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理,可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选,为找到新的选择性M1受体激动剂奠定了基础.     

CCK1受体的同源模拟和分子对接研究

采用同源建模法对CCK1受体的三维结构进行了模拟,并采用分子动力学方法对模型进行修正和优化,再采用与训练集激动剂和拮抗剂分子对接的方法分别得到激动状态和拮抗状态CCK1受体的三维结构模型。得到的模型使用DOCK对接软件对训练集中的分子进行对接,所得结果与其实际活性拟合度较好,说明我们建立的激动和拮抗状态下的CCK1受体的三维结构模型比较合理,可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选。     

A_1腺苷受体的同源模建及其结构验证

采用同源模建的方法构建了A1腺苷受体的三维结构,并与拮抗剂分子DPCPX对接,将得到的复合物结构进行5 ns的分子动力学模拟,以最后2 ns的平均结构和平衡后抽取的11帧构象共12个蛋白结构为研究对象,用包含52个活性分子和1000个诱饵分子的测试库,分别通过DOCK、VINA和GOLD三种对接软件进行评价,最终得出合理的蛋白质模型.根据top10%的富集因子(EF)和ROC曲线下面积(AU-ROC)的计算结果,我们认为GOLD是最适合A1腺苷受体的对接软件,而12个蛋白质结构中F5和Favg的三维结构模型比较合理,可以作为进一步大规模虚拟筛选的模型.     

距离比较法构建M1受体激动剂药效团模型

在M1受体三维结构未知的情况下,利用距离比较法(DISCO)对24个具有Mi受体激动活性的化合物进行了研究,构建了Mi受体激动剂可能的药效团模型,为设计新M1受体激动剂提供了参考,并以此为提问结构在ACD数据库和中草药数据系统(TCMDB)中进行搜索,得到一系列结构新颖并可能具有Mi激动活性的化合物.     

距离比较法构建M1受体激动剂药效团模型

在M1受体三维结构未知的情况下,利用距离比较法（DISCO）对24个具有M1受体激动活性的化合物进行了研究,构建了M1受体激动剂可能的药效团模型,为设计新M1受体激动剂提供了参考,并以此为提问结构在ACD数据库和中草药数据系统（TCMDB）中进行搜索,得到一系列结构新颖并可能具有M1激动活性的化合物.     

辛味中药与嗅觉受体相互作用的分子模拟

采用分子模拟的方法, 在Schrdinger软件平台上, 用同源模建的方法构建了嗅觉受体OR1D2, OR7D4和OR51E1的三维结构模型. 运用分子动力学模块Desmond将与激动剂以及抑制剂分别对接的嗅觉受体复合物置于磷脂双膜中进行模拟. 最后将辛味中药的小分子分别对接到嗅觉受体中, 并与苦味中药的对接结果相对照, 依据实验结果, 讨论辛味中药发挥作用的分子机制. 该研究着重于同源模建、分子动力学和分子对接技术的综合应用, 探讨辛味中药化学成分与嗅觉受体的相互作用及其分子机理, 为从分子层面揭示辛味中药的药效物质基础提供帮助, 也为中药药性的研究提供了新的思路和方法.     

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。     

芬太尼类化合物与阿片μ受体相互作用的分子对接与分子动力学模拟

采用分子对接和分子动力学(MD)模拟方法研究了芬太尼类化合物与阿片μ受体的相互作用机制.先用AutoDock4.0程序将芬太尼类化合物对接到同源模建的阿片μ受体结构中,再用GROMACS程序包在水溶液体系中分别对12个芬太尼激动剂和阿片μ受体蛋白复合物进行了MD模拟研究,优化对接复合物的结构,最后利用MM-PBSA方法,在APBS程序中计算芬太尼类衍生物与阿片μ受体的结合自由能,计算出的受体配合物结合常数(Ki)与其实验值吻合较好,并预测了化合物的活性排序.结果表明,复合物蛋白结构与空载受体蛋白结构有较大差异,特别是胞内区IL2、IL3和跨膜区段TM4骨架构象变化较大,不同的化合物对受体结构影响也有差异,活性较好的化合物会增加蛋白特定区域结构的柔性.芬太尼类化合物可能是通过和受体结合后诱导阿片μ受体构象转变为活性构象,引起一系列的信号传导激活G蛋白,从而引发生理效应.     

α1A-肾上腺素受体的同源模建及其对接研究

以β2肾上腺素受体(β2-AR)为模板,采用同源模建和分子动力学模拟构建了人类α1A-肾上腺素受体(α1A-AR)的三维结构模型,并利用PROCHECK,PROSA和WHAT-IF评估了模型的合理性.所得的结构采用分子对接程序Flexidock与激动剂去甲肾上腺素和拮抗剂西罗多辛分别进行对接,结果表明,2种配基具有相似...     

PPAR激动剂的定向设计、虚拟筛选及合成

过氧化物酶体增殖因子活化受体(PPAR)是核受体超家族的一员.基于受体结构的药物分子设计与组合化学策略相结合,构建了过氧化物酶体增殖因子活化受体(PPAR)激动剂的虚拟化合物库.将已知小分子配体(GW409544)与PPAR晶体复合物进行剥离,得到受体的活性构象,并利用此活性受体分子与虚拟库中小分子进行对接和虚拟筛选,得到理论上结合较强的化合物,并对这些化合物进行合成,共合成9个新化合物.活性筛选结果显示化合物对PPAR具有一定的亲和力,其中有三个化合物显示出对PPARα,PPARγ的双重激动作用,从而指导新活性化合物的设计和合成.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

An anchor-dependent molecular docking process for docking small flexible molecules into rigid protein receptors

A molecular docking method designated as ADDock, anchor- dependent molecular docking process for docking small flexible molecules into rigid protein receptors, is presented in this article. ADDock makes the bond connection lists for atoms based on anchors chosen for building molecular structures for docking small flexible molecules or ligands into rigid active sites of protein receptors. ADDock employs an extended version of piecewise linear potential for scoring the docked structures. Since no translational motion for small molecules is implemented during the docking process, ADDock searches the best docking result by systematically changing the anchors chosen, which are usually the single-edge connected nodes or terminal hydrogen atoms of ligands. ADDock takes intact ligand structures generated during the docking process for computing the docked scores; therefore, no energy minimization is required in the evaluation phase of docking. The docking accuracy by ADDock for 92 receptor-ligand complexes docked is 91.3%. All these complexes have been docked by other groups using other docking methods. The receptor-ligand steric interaction energies computed by ADDock for some sets of active and inactive compounds selected and docked into the same receptor active sites are apparently separated. These results show that based on the steric interaction energies computed between the docked structures and receptor active sites, ADDock is able to separate active from inactive compounds for both being docked into the same receptor.     

Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

This study was performed to find the selective chemical features for Aurora kinase-B inhibitors using the potent methods like Hip-Hop, virtual screening, homology modeling, molecular dynamics and docking. The best hypothesis, Hypo1 was validated toward a wide range of test set containing the selective inhibitors of Aurora kinase-B. Homology modeling and molecular dynamics studies were carried out to perform the molecular docking studies. The best hypothesis Hypo1 was used as a 3D query to screen the chemical databases. The screened molecules from the databases were sorted based on ADME and drug like properties. The selective hit compounds were docked and the hydrogen bond interactions with the critical amino acids present in Aurora kinase-B were compared with the chemical features present in the Hypo1. Finally, we suggest that the chemical features present in the Hypo1 are vital for a molecule to inhibit the Aurora kinase-B activity.     

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A,a selective kappa opioid receptor agonist

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based “agonist-bound” hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK _i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

De novo design based identification of potential HIV-1 integrase inhibitors: A pharmacoinformatics study

In the present study, pharmacoinformatics paradigms include receptor-based de novo design, virtual screening through molecular docking and molecular dynamics (MD) simulation are implemented to identify novel and promising HIV-1 integrase inhibitors. The de novodrug/ligand/molecule design is a powerful and effective approach to design a large number of novel and structurally diverse compounds with the required pharmacological profiles. A crystal structure of HIV-1 integrase bound with standard inhibitor BI-224436 is used and a set of 80,000 compounds through the de novo approach in LigBuilder is designed. Initially, a number of criteria including molecular docking, in-silico toxicity and pharmacokinetics profile assessments are implied to reduce the chemical space. Finally, four de novo designed molecules are proposed as potential HIV-1 integrase inhibitors based on comparative analyses. Notably, strong binding interactions have been identified between a few newly identified catalytic amino acid residues and proposed HIV-1 integrase inhibitors. For evaluation of the dynamic stability of the protein-ligand complexes, a number of parameters are explored from the 100 ns MD simulation study. The MD simulation study suggested that proposed molecules efficiently retained their molecular interaction and structural integrity inside the HIV-1 integrase. The binding free energy is calculated through the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach for all complexes and it also explains their thermodynamic stability. Hence, proposed molecules through de novo design might be critical to inhibiting the HIV-1 integrase.     

Structure of the virus capsid protein VP1 of enterovirus 71 predicted by some homology modeling and molecular docking studies

The homology modeling technique has been used to construct the structure of enterovirus 71 (EV 71) capsid protein VP1. The protein is consisted of 297 amino acid residues and treated as the target. The amino acid sequence identity between the target protein and sequences of template proteins 1EAH, 1PIV, and 1D4M searched from NCBI protein BLAST and WorkBench protein tools were 38, 37, and 36%, respectively. Based on these template structures, the protein model was constructed by using the InsightII/Homology program. The protein model was briefly refined by energy minimization and molecular dynamics (MD) simulation steps. The protein model was validated using some web available servers such as ERRAT, PROCHECK, PROVE, and PROSA2003. However, an inconsistency between the docking scores and the measured activity was observed for a series of EV 71 VP1 inhibitors synthesized by Shia et al. (J Med Chem 2002, 45, 1644) and docked into the binding pocket of the protein model using the DOCK 4.0.2 program. The protein model with an EV 71 VP1 inhibitor docked and engulfed was then refined further by some MD simulation steps in the presence of water molecules. The docking scores obtained for these inhibitors after such a MD refinement were well correlated with the activities. The structure-activity relationships for the ligand-protein model system was also analyzed using the GRID-VOLSURF programs and the corresponding noncrossvalidated and crossvalidated (by leave-one-out) r2 and q2 were 0.99 and 0.61, respectively. The hydrophobic nature of the binding pocket of the protein model was also examined using the GRID21 program. The possibility of improving the potency of the current series of EV 71 VP1 inhibitors was discussed based on all the studies presented.     

A comprehensive docking study on the selectivity of binding of aromatic compounds to proteins

Generally, computer-aided drug design is focused on screening of ligand molecules for a single protein target. The screening of several proteins for a ligand is a relatively new application of molecular docking. In the present study, complexes from the Brookhaven Protein Databank were used to investigate a docking approach of protein screening. Automated molecular docking calculations were applied to reproduce 44 protein-aromatic ligand complexes (31 different proteins and 39 different ligand molecules) of the databank. All ligands were docked to all different protein targets in altogether 12090 docking runs. Based on the results of the extensive docking simulations, two relative measures, the molecular interaction fingerprint (MIF) and the molecular affinity fingerprint (MAF), were introduced to describe the selectivity of aromatic ligands to different proteins. MIF and MAF patterns are in agreement with fragment and similarity considerations. Limitations and future extension of our approach are discussed.     

A pharmacoinformatic approach on Cannabinoid receptor 2 (CB2) and different small molecules: Homology modelling,molecular docking,MD simulations,drug designing and ADME analysis

CB2 receptor belongs to the family of G-protein coupled receptors (GPCRs), which extensively controls a range of pointer transduction. CB2 plays an essential role in the immune system. It also associates in the pathology of different ailment conditions. In this scenario, the synthetic drugs are inducing side effects to the human beings after the drug use. Therefore, this study is seeking novel alternate drug molecules with least side effects than conventional drugs. The alternative drug molecules were chosen from the natural sources. These molecules were selected from cyanobacteria with the help of earlier research findings. The target and ligand molecules were obtained from recognized databases. The bioactive molecules are selected from various cyanobacterial species, which are selected by their biological and pharmacological properties, after, which we incorporated to the crucial findings such as homology modelling, molecular docking, MD simulations along with absorption, distribution, metabolism, and excretion (ADME) analysis. Initially, the homology modelling was performed to frame the target from unknown sequences of CB2, which revealed 44% of similarities and 66% of identities with the A2A receptor. Subsequently, the CB2 protein molecule has docked with already known and prepared bioactive molecules, agonists and antagonist complex. In the present study, the agonists (5) and antagonist (1) were also taken for comparing the results with natural molecules. At the end of the docking analysis, the cyanobacterial molecules and an antagonist TNC-201 are revealed better docking scores with well binding contacts than the agonists. Especially, the usneoidone shows better results than other cyanobacterial molecules, and it is very close docking scores with that of TCN-201. Therefore, the usneoidone has incorporated to MD simulation with Cannabinoid receptors 2 (CB2). In MD simulations, the complex (CB2 and usneoidone) reveals better stability in 30 ns. Based on the computational outcome, we concluded that usneoidone is an effectual and appropriate drug candidate for activating CB2 receptors and it will be serving as a better component for the complications of CB2. Moreover, these computational approaches can be motivated to discover novel drug candidates in the pharmacological and healthcare sectors.