HMG-CoA还原酶抑制剂三维药效团的构建

以作用于鼠肝脏细胞的21个3-羟基-3-甲基戊二酰辅酶A(HMG-CoA)还原酶抑制剂(RI)为训练集, 训练集化合物具备结构多样性, 来源于相同药理模型, 活性值IC50范围在0.3-8000 nmol·L-1. 利用Catalyst 计算HMG-CoA还原酶抑制剂最优药效团由一个氢键受体, 一个氢键给体, 一个疏水基团和一个芳香环特征组成. 药效团模型Fixed cost值, Total cost值和Configuration cost值分别为88.75、111.5 和16.98. 训练集化合物活性计算值与实测值相关系数为0.8883, 偏差值为1.269, 交叉验证结果表明, 药效团模型具有较高的置信度, 对测试集化合物活性值的预测结果显示有较好的预测能力, 可用于数据库搜索发现新的具有该活性的化合物, 也可用于中药或天然产物药物的研究开发.     

A 3D QSAR approach to the search for geometrical similarity in a series of nonpeptide angiotensin II receptor antagonists

Summary A 3D QSAR methodology based on the combined use of conformational analysis and chemometrics was applied to perform a comparative analysis of the 3D conformational features of 13 nonpeptide angiotensin II receptor antagonists showing different levels of binding affinity. Conformational analysis by using a molecular mechanics MM2 method was carried out for each of these structures to obtain conformational minima. These minima were described by ten interatomic distances which define the relative spatial disposition of five significant atoms belonging to relevant functional groups present in all the 13 molecules. The structure-activity relationship between the interatomic distances and the biological activity was then assessed by using chemometric methods (cluster analysis, principal component analysis, classification methods). With our indirect approach based on the search for geometrical similarity it was possible, even though structural information on the receptor active site was lacking, to identify the 3D geometrical requirements for the binding affinity of nonpeptide angiotensin II receptor inhibitors.     

Structure-activity relationship of Ca2+ channel blockers: A study using conformational analysis and chemometric methods

Summary A structure-activity relationship study has been done on 8 compounds with the activity known as Ca²⁺ channel blockers. Conformational analysis was carried out using a molecular mechanics method. The 3D-QSAR approach was used and the most polar functional groups present in all the molecules were considered. Eight interatomic distances are necessary to define the relative spatial disposition of these relevant molecular fragments. The structure-activity relationship between interatomic distances and biological activity was performed using statistic and chemometric methods. In particular, with Principal Component Analysis, it was possible to reduce the number of interatomic distances: only six of the eight distances are sufficient to describe the system in a useful way. A classification method was iteratively used to select the most probable conformations linked to the biological activity and to build a model able to classify conformations according to their biological behaviour. Cluster analysis on the active selected conformations subsequently allowed the identification of two different geometrical patterns for the active compounds. Finally the validity of the model was verified by correctly predicting the activity of other molecules not used in the construction of the model but possessing known activity.     

A self-organizing algorithm for molecular alignment and pharmacophore development

We present a method for simultaneous three-dimensional (3D) structure generation and pharmacophore-based alignment using a self-organizing algorithm called Stochastic Proximity Embedding (SPE). Current flexible molecular alignment methods either start from a single low-energy structure for each molecule and tweak bonds or torsion angles, or choose from multiple conformations of each molecule. Methods that generate structures and align them iteratively (e.g., genetic algorithms) are often slow. In earlier work, we used SPE to generate good-quality 3D conformations by iteratively adjusting pairwise distances between atoms based on a set of geometric rules, and showed that it samples conformational space better and runs faster than earlier programs. In this work, we run SPE on the entire ensemble of molecules to be aligned. Additional information about which atoms or groups of atoms in each molecule correspond to points in the pharmacophore can come from an automatically generated hypothesis or be specified manually. We add distance terms to SPE to bring pharmacophore points from different molecules closer in space, and also to line up normal/direction vectors associated with these points. We also permit pharmacophore points to be constrained to lie near external coordinates from a binding site. The aligned 3D molecular structures are nearly correct if the pharmacophore hypothesis is chemically feasible; postprocessing by minimization of suitable distance and energy functions further improves the structures and weeds out infeasible hypotheses. The method can be used to test 3D pharmacophores for a diverse set of active ligands, starting from only a hypothesis about corresponding atoms or groups.     

Molecular docking and 3D-QSAR study of pyranmycin derivatives against 16S rRNA A site

To understand pharmacophore properties of pyranmycin derivatives and to design novel inhibitors of 16S rRNA A site, comparative molecular field analysis (CoMFA) approach was applied to analyze three-dimensional quantitative structure–activity relationship (3D-QSAR) of 17 compounds. AutoDock 3.0.5 program was employed to locate the orientations and conformations of the inhibitors interacting with 16S rRNA A site. The interaction mode was demonstrated in the aspects of inhibitor conformation, hydrogen bonding and electrostatic interaction. Similar binding conformations of these inhibitors and good correlations between the calculated binding free energies and experimental biological activities suggest that the binding conformations of these inhibitors derived from docking procedure were reasonable. Robust and predictive 3D-QSAR model was obtained by CoMFA with q² values of 0.723 and 0.993 for cross-validated and non-cross-validated, respectively. The 3D-QSAR model built here will provide clear guidelines for novel inhibitors design based on the Pyranmycin derivatives against 16S rRNA A site.     

Study of Aldo-keto Reductase 1C3 Inhibitor with Novel Framework for Treating Leukaemia Based on Virtual Screening and In vitro Biological Activity Testing

Aldo-keto reductase 1C3(AKR1C3) is a potential target for the treatment of acute myeloid leukaemia and T-cell acute lymphoblastic leukaemia. In this study, pharmacophore models, molecular docking and virtual screening of target prediction were used to find a potential AKR1C3 inhibitor. Firstly, eight bacteriocin derivatives(Z1-Z8) were selected as training sets to construct 20 pharmacophore models. The best pharmacophore model MODEL_016 was obtained by Decoy test(the enrichment degree was 21.5117, and the fitting optimisation degree was 0.9668). Secondly, MODEL_016 was used for the virtual screening of ZINC database. Thirdly, the hit 83256 molecules were docked into the AKR1C3 protein. Compared to the total scores and interactions between compounds and protein, 16532 candidate compounds with higher docking scores and interactions with important residues PHE306 and TRP227 were screened. Lastly, eight compounds(A1-A8) that had good absorption, distribution, metabolism, excretion and toxicity(ADMET) properties were obtained by target prediction. Compounds A3 and A7 with high total score and good target prediction results were selected for in vitro biological activity test, whose IC₅₀ values were 268.3 and 88.94 μmol/L, respectively. The results provide an important foundation for the discovery of novel AKR1C3 inhibitors. The research methods used in this study can also provide important references for the research and development of new drugs.     

A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists

Summary In the absence of a 3D structure of the target biomolecule, to propose the 3D requirements for a small molecule to exhibit a particular bioactivity, one must supply both a bioactive conformation and a superposition rule for every active compound. Our strategy identifies both simultaneously. We first generate and optimize all low-energy conformations by any suitable method. For each conformation we then use ALAD-DIN to calculate the location of points to be considered as part of the superposition. These points include atoms in the molecule and projections from the molecule to hydrogen-bond donors and acceptors or charged groups in the binding site. These positions and the relative energy of each conformation are the input to our new program DISCO. It uses a clique-detection method to find superpositions that contain a least one conformation of each molecule and user-specified numbers of point types and chirality. DISCO is fast; for example, it takes about 1 min CPU to propose pharmacophores from 21 conformations of seven molecules. We typically run DISCO several times to compare alternative pharmacophore maps. For D₂ dopamine agonists DISCO shows that the newer 2-aminothiazoles fit the traditional pharmacophore. Using site points correctly identifies the bioactive enantiomers of indoles to compare with catechols whereas using only ligand points leads to selecting the inactive enantiomer for the pharmacophore map. In addition, DISCO reproduces pharmacophore maps of benzodiazepines in the literature and proposes subtle improvements. Our experience suggests that clique-detection methods will find many applications in computational chemistry and computer-assisted molecular design.     

Modeling flexible pharmacophores with distance geometry, scoring, and bound stretching

The study of pharmacophores, i.e., of common features between different ligands, is important for the quantitative identification of "compatible" enzymes and binding species. A pharmacophore-based technique is developed that combines multiple conformations with a distance geometry method to create flexible pharmacophore representations. It uses a set of low-energy conformations combined with a new process we call bound stretching to create sets of distance bounds, which contain all or most of the low-energy conformations. The bounds can be obtained using the exact distances between pairs of atoms from the different low-energy conformations. To avoid missing conformations, we can take advantage of the triangle distance inequality between sets of three points to logically expand a set of upper and lower distance bounds (bound stretching). The flexible pharmacophore can be found using a 3-D maximal common subgraph method, which uses the overlap of distance bounds to determine the overlapping structure. A scoring routine is implemented to select the substructures with the largest overlap because there will typically be many overlaps with the maximum number of overlapping bounds. A case study is presented in which 3-D flexible pharmacophores are generated and used to eliminate potential binding species identified by a 2-D pharmacophore method. A second case study creates flexible pharmacophores from a set of thrombin ligands. These are used to compare the new method with existing pharmacophore identification software.     

Molecular dynamics simulation study of PTP1B with allosteric inhibitor and its application in receptor based pharmacophore modeling

Allosteric inhibition of protein tyrosine phosphatase 1B (PTP1B), has paved a new path to design specific inhibitors for PTP1B, which is an important drug target for the treatment of type II diabetes and obesity. The PTP1B_1–282-allosteric inhibitor complex crystal structure lacks α7 (287–298) and moreover there is no available 3D structure of PTP1B_1–298 in open form. As the interaction between α7 and α6–α3 helices plays a crucial role in allosteric inhibition, α7 was modeled to the PTP1B_1–282 in open form complexed with an allosteric inhibitor (compound-2) and a 5 ns MD simulation was performed to investigate the relative orientation of the α7–α6–α3 helices. The simulation conformational space was statistically sampled by clustering analyses. This approach was helpful to reveal certain clues on PTP1B allosteric inhibition. The simulation was also utilized in the generation of receptor based pharmacophore models to include the conformational flexibility of the protein-inhibitor complex. Three cluster representative structures of the highly populated clusters were selected for pharmacophore model generation. The three pharmacophore models were subsequently utilized for screening databases to retrieve molecules containing the features that complement the allosteric site. The retrieved hits were filtered based on certain drug-like properties and molecular docking simulations were performed in two different conformations of protein. Thus, performing MD simulation with α7 to investigate the changes at the allosteric site, then developing receptor based pharmacophore models and finally docking the retrieved hits into two distinct conformations will be a reliable methodology in identifying PTP1B allosteric inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.     

Liquid Chromatographic Determination of 3-Hydroxy-3-methylglutaryl Coenzyme A Reductase Inhibitors

Reversed-phase high-performance liquid chromatography (RP-HPLC) was used as a tool to explore the retention behavior and separation of four 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, namely compactin, lovastatin, simvastatin, and pravastatin in their hydroxy acid and lactone forms. The contribution of C-6 and C-2′ methyl groups and lactonization to the molecular hydrophobicity among these four structurally related HMG-CoA reductase inhibitors were elucidated. Eight components (four lactones and four hydroxy acids) could be resolved by RP-HPLC with isocratic elution. In a binary mobile phase system of acetonitrile-water containing 0.5% acetic acid, the free hydroxy acids and corresponding lactone forms remained intact and were completely separated. This study demonstrated that RP-HPLC is suitable for simultaneous determination of active and prodrug forms of these HMG-CoA reductase inhibitors.     

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.     

Identification of Influenza PAN Endonuclease Inhibitors via 3D-QSAR Modeling and Docking-Based Virtual Screening

Structural and biochemical studies elucidate that PA_N may contribute to the host protein shutdown observed during influenza A infection. Thus, inhibition of the endonuclease activity of viral RdRP is an attractive approach for novel antiviral therapy. In order to envisage structurally diverse novel compounds with better efficacy as PA_N endonuclease inhibitors, a ligand-based-pharmacophore model was developed using 3D-QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery Studio. As the training set, 25 compounds were taken to generate a significant pharmacophore model. The selected pharmacophore Hypo1 was further validated by 12 compounds in the test set and was used as a query model for further screening of 1916 compounds containing 71 HIV-1 integrase inhibitors, 37 antibacterial inhibitors, 131 antiviral inhibitors and other 1677 approved drugs by the FDA. Then, six compounds (Hit01–Hit06) with estimated activity values less than 10 μM were subjected to ADMET study and toxicity assessment. Only one potential inhibitory ‘hit’ molecule (Hit01, raltegravir’s derivative) was further scrutinized by molecular docking analysis on the active site of PA_N endonuclease (PDB ID: 6E6W). Hit01 was utilized for designing novel potential PA_N endonuclease inhibitors through lead optimization, and then compounds were screened by pharmacophore Hypo1 and docking studies. Six raltegravir’s derivatives with significant estimated activity values and docking scores were obtained. Further, these results certainly do not confirm or indicate the seven compounds (Hit01, Hit07, Hit08, Hit09, Hit10, Hit11 and Hit12) have antiviral activity, and extensive wet-laboratory experimentation is needed to transmute these compounds into clinical drugs.     

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.     

GABAA受体萜类抑制剂构效关系研究

采用DISCOtech方法建立了大鼠和家蝇GABAA受体萜类抑制剂的药效团模型, 根据药效团模型叠加规则建立了大鼠和家蝇GABAA受体萜类抑制剂CoMFA模型, 模型的交叉验证相关系数分别为0.713和0.738. 构效关系研究显示， 家蝇和大鼠受体抑制剂结合部位之间存在一个主要差别: 与受体作用的抑制剂的负电荷基团取代有利于保持其对哺乳动物的高抑制活性, 而保持对昆虫的高抑制活性是不需要的, 从而为寻找先导化合物和设计高选择性杀虫剂提供了理论指导.     

Theoretical studies of the electronic and structural features of the fragments of dihydropholate reductase inhibitors

The effects of the structural characteristics of dihydrofolate reductase (DHFR) inhibitors on their tuberculostatic activity have been analyzed. It was shown that an increase in the electron density on bonds and atoms in the ring led to an increase in the biological activity of the compounds. A correlation was found between the biological activity and the characteristics of the critical points of electron density of bonds. The 3D- and 4D-QSAR studies with the CiS algorithm revealed the pharmacophore and antipharmacophore fragments of DHFR inhibitors, and regions of the receptor that are responsible for the biological action of dihydropyrimidines were found. Receptor ligand complexes were modeled. For a series of drugs containing a podand chain, it was found that the chain performed only the transport membranotropic function because the increase in the size of molecules due to the podand chain gives rise to steric hindrances when the chain is built in the receptor cavity.     

In Silico Strategy for Targeting the mTOR Kinase at Rapamycin Binding Site by Small Molecules

Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified structure-based pharmacophore hypothesis was built starting from the FKBP12-rapamycin-FRB ternary complex retrieved from RCSB Protein Data Bank (PDB code 1FAP). Then, the obtained model was used as filter to screen the ZINC biogenic compounds library, containing molecules derived from natural sources or natural-inspired compounds. The resulting hits were clustered according to their similarity; moreover, compounds showing the highest pharmacophore fit-score were chosen from each cluster. The selected molecules were subjected to docking studies to clarify their putative binding mode. The binding free energy of the obtained complexes was calculated by MM/GBSA method and the hits characterized by the lowest ΔG_bind values were identified as potential mTOR inhibitors. Furthermore, the stability of the resulting complexes was studied by means of MD simulation which revealed that the selected compounds were able to form a stable ternary complex with FKBP12 and FRB domain, thus underlining their potential ability to inhibit mTOR with a rapamycin-like mechanism.