In silico prediction of receptor-mediated environmental toxic phenomena-application to endocrine disruption

It is an objective of our institution to establish a virtual laboratory allowing for a reliable in silico estimation of the harmful effects triggered by drugs, chemicals and their metabolites. In the recent past, we have developed the underlying technology (Multi-dimensional QSAR: Quasar and Raptor) and compiled a pilot system including the 3D models of three receptors known to mediate endocrine-disrupting effects (the aryl hydrocarbon receptor, the estrogen receptor and the androgen receptor, respectively) and validated them against 310 compounds (drugs, chemicals, toxins). Within this set up we could demonstrate that our concepts are able to both recognize toxic compounds substantially different from those used in the training set as well as to classify harmless compounds clearly as being non-toxic. This suggests that our approach can be used for the prediction of adverse effects of drug molecules and chemicals.     

A 3D QSAR study on a set of dopamine D4 receptor antagonists

The molecular alignments obtained from a previously reported pharmacophore model have been employed in a three-dimensional quantitative structure-activity relationship (3D QSAR) study, to obtain a more detailed insight into the structure-activity relationships for D(2) and D(4) receptor antagonists. The frequently applied CoMFA method and the related CoMSIA method were used. Statistically significant models have been derived with these two methods, based on a set of 32 structurally diverse D(2) and D(4) receptor antagonists. The CoMSIA and the CoMFA methods produced equally good models expressed in terms of q(2) values. The predictive power of the derived models were demonstrated to be high. Graphical interpretation of the results, provided by the CoMSIA method, brings to light important structural features of the compounds related to either low- or high-affinity D(2) or D(4) antagonism. The results of the 3D QSAR studies indicate that bulky N-substituents decrease D(2) binding, whereas D(4) binding is enhanced. Electrostatically favorable and unfavorable regions exclusive to D(2) receptor binding were identified. Likewise, certain hydrogen-bond acceptors can be used to lower D(2) affinity. These observations may be exploited for the design of novel dopamine D(4) selective antagonists.     

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.     

Quantitative structure-based design: formalism and application of receptor-dependent RD-4D-QSAR analysis to a set of glucose analogue inhibitors of glycogen phosphorylase

A method for performing quantitative structure-based design has been developed by extending the current receptor-independent RI-4D-QSAR methodology to include receptor geometry. The resultant receptor-dependent RD-4D-QSAR approach employs a novel receptor-pruning technique to permit effective processing of ligands with the lining of the binding site wrapped about them. Data reduction, QSAR model construction, and identification of possible pharmacophore sites are achieved by a three-step statistical analysis consisting of genetic algorithm optimization followed by backward elimination multidimensional regression and ending with another genetic algorithm optimization. The RD-4D-QSAR method is applied to a series of glucose inhibitors of glycogen phosphorylase b, GPb. The statistical quality of the best RI- and RD-4D-QSAR models are about the same. However, the predictivity of the RD- model is quite superior to that of the RI-4D-QSAR model for a test set. The superior predictive performance of the RD- model is due to its dependence on receptor geometry. There is a unique induced-fit between each inhibitor and the GPb binding site. This induced-fit results in the side chain of Asn-284 serving as both a hydrogen bond acceptor and donor site depending upon inhibitor structure. The RD-4D-QSAR model strongly suggests that quantitative structure-based design cannot be successful unless the receptor is allowed to be completely flexible.     

Toward the identification of a reliable 3D QSAR pharmacophore model for the CCK2 receptor antagonism

The present study describes application of computational approaches to identify a validated and reliable 3D QSAR pharmacophore model for the CCK-2R antagonism through integrated ligand and structure based studies using anthranilic sulfonamide and 1,3,4-benzotriazepine based CCK-2R antagonists. The best hypothesis consisted five features viz. two aliphatic hydrophobic, one aromatic hydrophobic, one H-bond acceptor, and one ring aromatic feature with an excellent correlation for 34 training set (r2(training) = 0.83) and 58 test set compounds (r2(test) = 0.74). This model was validated through F-test and docking studies at the active site of the plausible CCK-2R where the 99% significance and well corroboration with the pharmacophore model respectively describes the model's reliability. The model also predicts well to other known clinically effective CCK-2R antagonists. Therefore, the developed model may useful in finding new scaffolds that may aid in design and develop new chemical entities (NCEs) as potent CCK-2R antagonists before their synthesis.     

酪氨酸激酶抑制剂的三维定量构效关系研究

利用柔性原子受体模型(FLARM)方法对一系列的异黄酮和喹诺酮衍生物表皮生长因子受体酪氨酸激酶抑制剂进行了三维定量构效关系研究，得到了合理的构效关系模型.FLARM方法的计算结果还给出了虚拟的受体模型，该模型说明了抑制剂与受体之间可能的相互作用.由该虚拟受体模型得到的受体-配体相互作用与Novartis药效团模型比较类似.     

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ～75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.     

Pseudoreceptor models and 3D-QSAR for imidazobenzodiazepines at GABA A/BzR subtypes alphaxbeta3gamma2 [x = 1-3, 5, and 6] via flexible atom receptor model

Since benzodiazepines have been used widely in the treatment of anxiety, sleeplessness, and epilepsy, the receptor sites for the benzodiazepine are of prime importance. Quantitative structure-activity relationship (QSAR) studies and receptor modeling via Flexible Atom Receptor Model (FLARM) for the binding affinities of a series of imidazobenzodiazepines at five recombinant receptor subtypes were carried out successfully. The 3D-QSAR models for all five receptor subtypes were examined by a set of test set and demonstrated their high predictability for affinities of imidazobenzodiazepines at five receptor subtypes. The pseudoreceptors yielded by FLARM were compared to the united pharmacophore/receptor model. The result shows that two hydrogen bonds and other regions in the united pharmacophore/receptor model are presented in the pseudoreceptors, which demonstrates the receptor modeling capability of FLARM. The models and pseudoreceptors can help design high affinity ligands on the GABA(A)/BZ receptor and understand the GABA(A) receptor.     

丙型肝炎病毒抑制剂的三维药效团和构效关系

通过ＣＡＴＡＬＹＳＴ软件包得到了两类ＨＣＶ ＮＳ３丝氨酸蛋白酶抑制剂的三维药效团模型。尽管这两类抑制剂具有完全不同的骨架结构,但得到的药效团却具有共同的特性。这当这两类抑制剂和受体发生相互作用时,可能采用了相似的结合模式。根据药效团模型,进行了三维构效关系的研究。结果表明,得到的药效团模型具有良好的预测能力（线性回归系数Ｒ＝０．８９）。     

A promising in silico protocol to develop novel PPARγ antagonists as potential anticancer agents: Design,synthesis and experimental validation via PPARγ protein activity and competitive binding assay

Peroxisome proliferator-activated receptor gamma (PPARγ), a member of the nuclear receptor superfamily is an excellent example of targets that orchestrates cancer, inflammation, lipid and glucose metabolism. We report a protocol for the development of novel PPARγ antagonists by employing 3D QSAR based virtual screening for the identification of ligands with anticancer properties. The models are generated based on a large and diverse set of PPARγ antagonist ligands by the HYPOGEN algorithm using Discovery Studio 2019 drug design software. Among the 10 hypotheses generated, Hypotheses 2 showed the highest correlation coefficient values of 0.95 with less RMS deviation of 1.193. Validation of the developed pharmacophore model was performed by Fischer’s randomization and screening against test and decoy set. The GH score or goodness score was found to be 0.81 indicating moderate to a good model. The selected pharmacophore model Hypo 2 was used as a query model for further screening of 11,145 compounds from the PubChem, sc-PDB structure database, and designed novel ligands. Based on fit values and ADMET filter, the final 10 compounds with the predicated activity of ≤ 3 nM were subjected for docking analysis. Docking analysis revealed the unique binding mode with hydrophobic amino acid that can cause destabilization of the H12 which is an important molecular mechanism to prove its antagonist action. Based on high CDocker scores, Cpd31 was synthesized, purified, analyzed and screened for PPARγ competitive binding by TR-FRET assay. The biochemical protein binding results matched the predicted results. Further, Cpd31 was screened against cancer cells and validated the results.     

The identification of ligand features essential for PXR activation by pharmacophore modeling

Drug metabolizing enzymes and transporters are often involved in clinically relevant drug-drug interactions. These functional proteins can be induced by a wide range of xenobiotics. The induction is mediated by a group of receptors known as orphan nuclear receptors. The pregnane X receptor (PXR) is a member of this receptor family and regulates the expression of multiple Cytochrome P450 enzyme families (e.g. CYP 3A and 2B), phase II enzymes (e.g. UDP glucuronosyl transferases), and transporters (e.g. multidrug resistance protein 1). The software package Catalyst was employed to derive pharmacophore models for PXR activation. A structure based pharmacophore hypothesis and several ligand based ones were compared in order to identify ligand receptor interactions essential for receptor activation. The results suggest that hydrogen bonding to Gln285 is indispensable for PXR activation. Most ligands were found to form a second hydrogen bond to His407. Hydrophobic interactions are not essential for receptor activation but contribute to ligand affinity. Highly active compounds share up to five hydrophobic features that allow the ligand to occupy large areas of the predominantly hydrophobic binding pocket.     

Fuzzy tricentric pharmacophore fingerprints. 2. Application of topological fuzzy pharmacophore triplets in quantitative structure-activity relationships

Topological fuzzy pharmacophore triplets (2D-FPT), using the number of interposed bonds to measure separation between the atoms representing pharmacophore types, were employed to establish and validate quantitative structure-activity relationships (QSAR). Thirteen data sets for which state-of-the-art QSAR models were reported in literature were revisited in order to benchmark 2D-FPT biological activity-explaining propensities. Linear and nonlinear QSAR models were constructed for each compound series (following the original author's splitting into training/validation subsets) with three different 2D-FPT versions, using the genetic algorithm-driven Stochastic QSAR sampler (SQS) to pick relevant triplets and fit their coefficients. 2D-FPT QSARs are computationally cheap, interpretable, and perform well in benchmarking. In a majority of cases (10/13), default 2D-FPT models validated better than or as well as the best among those reported, including 3D overlay-dependent approaches. Most of the analogues series, either unaffected by protonation equilibria or unambiguously adopting expected protonation states, were equally well described by rule- or pKa-based pharmacophore flagging. Thermolysin inhibitors represent a notable exception: pKa-based flagging boosts model quality, although--surprisingly--not due to proteolytic equilibrium effects. The optimal degree of 2D-FPT fuzziness is compound set dependent. This work further confirmed the higher robustness of nonlinear over linear SQS models. In spite of the wealth of studied sets, benchmarking is nevertheless flawed by low intraset diversity: a whole series of thereby caused artifacts were evidenced, implicitly raising questions about the way QSAR studies are conducted nowadays. An in-depth investigation of thrombin inhibition models revealed that some of the selected triplets make sense (one of these stands for a topological pharmacophore covering the P1 and P2 binding pockets). Nevertheless, equations were either unable to predict the activity of the structurally different ligands or tended to indiscriminately predict any compound outside the training family to be active. 2D-FPT QSARs do however not depend on any common scaffold required for molecule superimposition and may in principle be trained on hand of diverse sets, which is a must in order to obtain widely applicable models. Adding (assumed) inactives of various families for training enabled discovery of models that specifically recognize the structurally different actives.     

Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism

Receptor-dependent (RD) 4D-QSAR models were constructed for a set of 39 4-hydroxy-5,6-dihydropyrone analogue HIV-1 protease inhibitors. The receptor model used in this QSAR analysis was derived from the HIV-1 protease (PDB ID ) crystal structure. The bound ligand in the active site of the enzyme, also a 4-hydroxy-5,6-dihydropyrone analogue, was used as the reference ligand for docking the data set compounds. The optimized RD 4D-QSAR models are not only statistically significant (r(2) = 0.86, q(2) = 0.80 for four- and greater-term models) but also possess reasonable predictivity based on test set predictions. The proposed "active" conformations of the docked analogues in the active site of the enzyme are consistent in overall molecular shape with those suggested from crystallographic studies. Moreover, the RD 4D-QSAR models also "capture" the existence of specific induced-fit interactions between the enzyme active site and each specific inhibitor. Hydrophobic interactions, steric shape requirements, and hydrogen bonding of the 4-hydroxy-5,6-dihydropyrone analogues with the HIV-1 protease binding site model dominate the RD 4D-QSAR models in a manner again consistent with experimental conclusions. Some possible hypotheses for the development of new lead HIV-1 protease inhibitors can be inferred from the RD 4D-QSAR models.     

基于结构和药效团特征的人类腺苷受体拮抗剂选择性比较

腺苷受体是重要的治疗靶标,选择性腺苷受体拮抗剂具有广泛的临床应用前景.本文通过同源模建构建了腺苷A₁、A_2B和A₃受体的结构,采用LigandScout 3.12软件分别构建了腺苷受体四种亚型的拮抗剂药效团模型.然后利用Schrödinger程序中的Induced Fit Docking模块完成受体-拮抗剂结合模式的预测,并与药效团结果进行比对.结果发现,由于结合口袋部位的残基在家族间高度保守,模建得到的各个亚型受体的初始结构活性口袋部位极为相似,无法用于亚型选择性拮抗剂的识别.而腺苷受体四种亚型拮抗剂药效团的药效特征与空间排布都不同,并与以前突变实验信息相吻合.研究结果说明,结合口袋部位的优化是模建中的关键步骤,基于配体的药效团模型所包含的一系列药效特征元素如氢键受体、氢键供体、疏水基团、芳环中心,可以很好地表征受体结合部位氢键、疏水空腔的位置及其方向.本文研究结果可以为进一步的优化同源模建结果,寻找新型的人类腺苷受体选择性拮抗剂提供理论依据.     

Total synthesis of two photoactivatable analogues of the growth-factor-like mediator sphingosine 1-phosphate: differential interaction with protein targets

The first synthesis of two photoreactive analogues of the lipid mediator and second messenger sphingosine 1-phosphate (S1P), [(32)P]-labeled (2S,3R)-14-O-(4'-benzoylphenyl)- and (2S,3R)-14-O-((4'-trifluoromethyldiazirinyl)phenyl)-(4E)-tetradecenyl-2-amino-3-hydroxy-1-phosphate, is described. The interactions of these probes with the S1P type-1 receptor (S1P(1)) transfected into membranes of rat hepatoma cells and with plasma proteins were analyzed. The S1P(1) receptor interacted in a specific manner with the benzophenone-containing ligand (K(D) = 84 +/- 10 nM vs K(D) for S1P = 36 +/- 2 nM); in contrast, no saturable specific binding was found with the diazirine-containing ligand. However, the same pattern was found for labeling of plasma proteins by both probes, indicating that different parts of the S1P pharmacophore underlie the interaction of S1P with its receptor and plasma carrier proteins.     

3D QSAR selectivity analyses of carbonic anhydrase inhibitors: insights for the design of isozyme selective inhibitors

A 3D QSAR selectivity analysis of carbonic anhydrase (CA) inhibitors using a data set of 87 CA inhibitors is reported. After ligand minimization in the binding pockets of CA I, CA II, and CA IV isoforms, selectivity CoMFA and CoMSIA 3D QSAR models have been derived by taking the affinity differences (DeltapKi) with respect to two CA isozymes as independent variables. Evaluation of the developed 3D QSAR selectivity models allows us to determine amino acids in the respective CA isozymes that possibly play a crucial role for selective inhibition of these isozymes. We further combined the ligand-based 3D QSAR models with the docking program AUTODOCK in order to screen for novel CA inhibitors. Correct binding modes are predicted for various CA inhibitors with respect to known crystal structures. Furthermore, in combination with the developed 3D QSAR models we could successfully estimate the affinity of CA inhibitors even in cases where the applied scoring function failed. This novel strategy to combine AUTODOCK poses with CoMFA/CoMSIA 3D QSAR models can be used as a guideline to assess the relevance of generated binding modes and to accurately predict the binding affinity of newly designed CA inhibitors that could play a crucial role in the treatment of pathologies such as tumors, obesity, or glaucoma.