The effect of antagonists on the conformational exchange of the retinoid X receptor alpha ligand‐binding domain

The effect of retinoid X receptor (RXR) antagonists on the conformational exchange of the RXR ligand‐binding domain (LBD) remains poorly characterized. To address this question, we used nuclear magnetic resonance spectroscopy to compare the chemical shift perturbations induced by RXR antagonists and agonists on the RXRα LBD when partnered with itself as a homodimer and as the heterodimeric partner with the peroxisome proliferator‐activated receptor γ (PPARγ) LBD. Chemical shift mapping on the crystal structure showed that agonist binding abolished a line‐broadening effect caused by a conformational exchange on backbone amide signals for residues in helix H3 and other regions of either the homo‐ or hetero‐dimer, whereas binding of antagonists with similar binding affinities failed to do so. A lineshape analysis of a glucocorticoid receptor‐interacting protein 1 NR box 2 coactivator peptide showed that the antagonists enhanced peptide binding to the RXRα LBD homodimer, but to a lesser extent than that enhanced by the agonists. This was further supported by a lineshape analysis of the RXR C‐terminal residue, threonine 462 (T462) in the homodimer but not in the heterodimer. Contrary to the agonists, the antagonists failed to abolish a line‐broadening effect caused by a conformational exchange on the T462 signal corresponding to the RXRα LBD–antagonist–peptide ternary complex. These results suggest that the antagonists lack the ability of the agonists to shift the equilibrium of multiple RXRα LBD conformations in favor of a compact state, and that a PPARγ LBD‐agonist complex can prevent the antagonist from enhancing the RXRα LBD‐coactivator binding interaction. Copyright © 2009 John Wiley & Sons, Ltd.     

AIScore chemically diverse empirical scoring function employing quantum chemical binding energies of hydrogen-bonded complexes

In this work we report on a novel scoring function that is based on the LUDI model and focuses on the prediction of binding affinities. AIScore extends the original FlexX scoring function using a chemically diverse set of hydrogen-bonded interactions derived from extensive quantum chemical ab initio calculations. Furthermore, we introduce an algorithmic extension for the treatment of multifurcated hydrogen bonds (XFurcate). Charged and resonance-assisted hydrogen bond energies and hydrophobic interactions as well as a scaling factor for implicit solvation were fitted to experimental data. To this end, we assembled a set of 101 protein-ligand complexes with known experimental binding affinities. Tightly bound water molecules in the active site were considered to be an integral part of the binding pocket. Compared to the original FlexX scoring function, AIScore significantly improves the prediction of the binding free energies of the complexes in their native crystal structures. In combination with XFurcate, AIScore yields a Pearson correlation coefficient of R P = 0.87 on the training set. In a validation run on the PDBbind test set we achieved an R P value of 0.46 for 799 attractively scored complexes, compared to a value of R P = 0.17 and 739 bound complexes obtained with the FlexX original scoring function. The redocking capability of AIScore, on the other hand, does not fully reach the good performance of the original FlexX scoring function. This finding suggests that AIScore should rather be used for postscoring in combination with the standard FlexX incremental ligand construction scheme.     

Interpretation of scoring functions using 3D molecular fields. Mapping the diacyl-hydrazine-binding pocket of an insect ecdysone receptor

A method is presented for the interpretation of receptor docking score values (rough measures of binding affinities) of ligands in terms of 3D molecular field interaction contributions. The FlexX and FlexX-Pharm methods were used to dock the structures of designed sets of ligands into the ligand-binding pocket of a selected receptor. In the next step the relationship was investigated between the FlexX and CScore scores and 3D molecular fields obtained for the docked conformations of the ligands, using the CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) methods. The approach yielded highly significant CoMFA and CoMSIA models demonstrating that a high portion of the variance in the docking score values of the ligands can be explained by steric, electrostatic, hydrophobic, and hydrogen bond donor and acceptor molecular field interaction contributions. The approach was exemplified by using the crystal structure of the ligand-binding domain of the ecdysone receptor (EcR) of the moth Heliotis virescens as well as virtual molecule libraries of analogues of known diacyl-hydrazine (DAH) type ecdysteroid agonists. By docking appropriately designed virtual compound libraries into the DAH binding pocket of EcR followed by CoMFA and CoMSIA of the docked conformations, hitherto unexplored regions of the receptor cavity could be mapped. By mapping the significant molecular field interaction contributions onto the model of the receptor-ligand complex, important receptor-ligand interactions could be highlighted that may help the design of novel highly scored receptor ligands. An advantage of the method is that no experimental biological activity data are required to exhaustively map the receptor-binding site.     

酪氨酸蛋白磷酸酯酶1B抑制剂的分子对接和三维定量构效关系研究

用一种柔性分子对接方法(FlexX)将12个2-草酰胺苯甲酸类抑制剂和酪氨酸蛋白磷酸酯酶(PTP1B)活性口袋进行分子对接,对接程序预测的抑制剂和酶之间的相互作用能与抑制活性之间有很好的相关性(非线性相关系数R²达0.859),这说明对接结果可以比较准确地预测抑制剂和PTP1B之间的结合模式.然后,将33个同类抑制剂的骨架叠合在分子对接预测的结合构象上,用比较分子力场分析方法(CoMFA)对其进行三维定量活性构效关系研究,得到的CoMFA模型具有很好的统计相关性(交互验证回归系数q²为0.650),并可以准确地预测测试集6个化合物的活性(平均标准偏差为0.177).同时,由CoMFA模型得出的抑制剂改造信息与用FlexX预测的结合模式是一致的,进一步证明我们预测的结合模式是正确的.为研究这类抑制剂和PTP1B的结合模式及对抑制剂进行结构改造提供了信息.     

Identification of novel PPARα/γ dual agonists by pharmacophore screening,docking analysis,ADMET prediction and molecular dynamics simulations

PPARα and PPARγ play an important role in regulating glucose and lipid metabolism. The single and selective PPARα or PPARγ agonists have caused several side effects such as edema, weight gain and cardiac failure. In the recent years, the dual PPARs agonist development has become a hot topic in the antidiabetic medicinal chemistry field. In this paper, the compound CHEMBL230490 were gained from CHEMBL database, by means of complex-based pharmacophore (CBP) virtual screening, molecular docking, ADMET prediction and molecular dynamics (MD) simulations. The compound CHEMBL230490 not only displayed higher binding scores and better binding modes with the active site of PPARα a/γ, but also had more favorable the pharmacokinetic properties and toxicity evaluated by ADMET prediction. The representative compound CHEMBL230490 was performed to MDs for studying a stable binding conformation. The results indicated that the CHEMBL230490 might be a potential antidiabetic lead compound. The research provided a valuable approach in developing novel PPARα/γ dual agonists for the treatment of type 2 diabetes mellitus (T2DM).     

Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors

An increasing number of docking/scoring programs are available that use different sampling and scoring algorithms. A reliable scoring function is the crucial element of such approaches. Comparative studies are needed to evaluate their current capabilities. DOCK4 with force field and PMF scoring as well as FlexX were used to evaluate the predictive power of these docking/scoring approaches to identify the correct binding mode of 61 MMP-3 inhibitors in a crystal structure of stromelysin and also to rank them according to their different binding affinities. It was found that DOCK4/PMF scoring performs significantly better than FlexX and DOCK4/FF in both ranking ligands and predicting their binding modes. Most notably, DOCK4/PMF was the only scoring/docking approach that found a significant correlation between binding affinity and predicted score of the docked inhibitors. However, comparing only those cases where the correct binding mode was identified (scoring highest among sampled poses), FlexX showed the best `fine tuning' (lowest rmsd) in predicted binding modes. The results suggest that not so much the sampling procedure but rather the scoring function is the crucial element of a docking program.     

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.     

Synthesis of novel PPARα/γ dual agonists as potential drugs for the treatment of the metabolic syndrome and diabetes type II designed using a new de novo design program PROTOBUILD

Peroxisome proliferator activated receptors (PPARs) have been shown to have critical roles in fatty acid oxidation, triglyceride synthesis, and lipid metabolism - making them an important target in drug discovery. Here we describe the in silico design, synthesis and in vitro characterisation of a novel series of 2,5-disubstituted indoles as PPARα/γ dual agonists. PPAR activation assays are performed with known agonists diazabenzene (WY14.643), aminopyridine (BRL49653) and bisaryl (L165.041), as positive controls. All the indole compounds synthesized are found to be active PPARα and PPARγ agonists, with particular efficacy from those with 2-naphthylmethyl substitution. This is a useful demonstration of a new de novo design methodology implemented by the protobuild program and its ability to rapidly produce novel modulators for a well characterized drug target.     

Synthesis of urea acetates as potential PPARα/γ,dual agonists

In the quest for novel PPARα/γ dual agonists as putative drugs for the treatment of type 2 diabetes and dyslipidemia,we designed and synthesized a series of urea acetates as potential PPARα/γ dual agonists.The structure of the target compounds,intermediates were characterized by 1H NMR,HRMS.     

Theoretical studies on opioid receptors and ligands. I. Molecular modeling and QSAR studies on the interaction mechanism of fentanyl analogs binding to μ‐opioid receptor

Based on our previous result of the three‐dimensional model of the μ‐opioid receptor, binding conformations of 13 fentanyl analogs and three‐dimensional structures for the complexs of these analogs with μ‐opioid receptor were constructed employing the molecular modeling method and our binding conformation search program for ligands (BCSPL). Energetic calculation and quantitative structure–activity relationship (QSAR) analysis indicated a good correlation between the calculated binding energies of fentanyl analogs and their binding affinities, pK_i's and pK's, and analgesic activities, − log ED₅₀'s. Based on the three‐dimensional models, the possible interaction mechanism of fentanyl analogs with μ‐opioid receptor can be illustrated and the available structure–activity relationship of these analgesic agents can be explained reasonably. © 2000 John Wiley & Sons, Inc. Int J Quant Chem 78: 285–293, 2000     

Definition of a pharmacophore for partial agonists of serotonin 5-HT3 receptors

A definition of a partial agonists serotonin 5-HT3 pharmacophore was carried out by considering a three-dimensional model which correlates the chemical structures of series of piperazinopyrrolothienopyrazines, piperazinopyridopyrrolopyrazines, piperazinopyrroloquinolaxines, piperazinopyridopyrroloquinoxalines, aminoalkyloximinopyrroloindoles, aminoalkyloximinothienopyrrolizines, and aminoalkyloximinopyrrolizines with the biological affinities. The model is formed by five features corresponding to two hydrogen bond acceptors, one aromatic ring, one hydrophobic group, and one positive ionizable site (quaternary ammonium ions). The nature of the features and the distances between them explain the partial agonist activities of these compounds.     

雌激素β受体喹啉类配体的分子对接及3D-QSAR研究

对33个喹啉衍生物的雌激素β受体活性进行了分子对接以及比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA). 对接结果显示氢键和疏水作用是配体与受体结合的主要因素,同时结果亦显示对接结合能与观测值pIC50具有极显著的线性相关性. 根据对接后各优势构象将33个样本进行叠合并进行CoMFA与CoMSIA研究,均得到了较优的结果,其中以选用立体场、静电场和疏水场建立的CoMSIA模型结果最优,其主成分数,r2,q2（LOO）和r2pred分别为2, 0.894, 0.708和0.802. 构效关系模型分析显示基团的空间位阻、电性及疏水作用是影响活性的主要因素     

Molecular modeling study of species-selective peroxisome proliferator-activated receptor (PPAR) alpha agonist; possible mechanism(s) of human PPARalpha selectivity of an alpha-substituted phenylpropanoic acid derivative (KCL)

In order to investigate the reason why phenylpropanoic acid derivative (KCL), a potent, human peroxisome proliferator-activated receptor (PPAR) alpha-selective agonist, shows this selectivity, we analyzed the binding modes of KCL and a related compound to the ligand-binding domain of human PPARalpha and rat PPARalpha by means of computer-aided molecular modeling. We concluded that the characteristic specificity of KCL is due to a specific hydrophobic contact between the hydrophobic tail part (the 4-trifluoromethyl group) and the key amino acid Ile272 located on the helix three region of the human PPARalpha ligand binding domain. We propose a possible binding mode of KCL with the ligand-binding domain of human PPARalpha. This binding model should offer important insights for further structural design of subtype-selective PPARalpha agonists for the treatment of altered metabolic homeostasis, such as dyslipidemia, obesity, and diabetes.     

Molecular modelling studies on the ORL1-receptor and ORL1-agonists

The ORLI (opioid receptor like 1)- receptor is a member of the family of rhodopsin-like G protein-coupled receptors (GPCR) and represents an interesting new therapeutical target since it is involved in a variety of biomedical important processes, such as anxiety, nociception, feeding, and memory. In order to shed light on the molecular basis of the interactions of the GPCR with its ligands, the receptor protein and a dataset of specific agonists were examined using molecular modelling methods. For that purpose, the conformational space of a very potent non-peptide ORL1-receptor agonist (Ro 64-6198) with a small number of rotatable bonds was analysed in order to derive a pharmacophoric arrangement. The conformational analyses yielded a conformation that served as template for the superposition of a set of related analogues. Structural superposition was achieved by employing the program FlexS. Using the experimental binding data and the superposition of the ligands, a 3D-QSAR analysis applying the GRID/GOLPE method was carried out. After the ligand-based modelling approach, a 3D model of the ORL1-receptor has been constructed using homology modelling methods based on the crystal structure of bovine rhodopsin. A representative structure of the model taken from molecular dynamics simulations was used for a manual docking procedure. Asp-130 and Thr-305 within the ORL1-receptor model served as important hydrophilic interaction partners. Furthermore, a hydrophobic cavity was identified stabilizing the agonists within their binding site. The manual docking results were supported using FlexX, which identified the same protein-ligand interaction points.     

Synthesis and biological evaluation of 5’-phenyl-3’H-spiro-[indoline-3,2’-[1,3,4]oxadiazol]-2-one analogs

A series of 5'-phenyl-3'H-spiro[indoline-3,2'-[1,3,4]thiadiazol]-2-one analogs were synthesized and their Bcl-2 protein inhibitory activities were studied. The lead compound was originally identified using a fluorescence polarization-based competitive binding assay. Among the 10 compounds investigated, 1k showed good binding affinities to Bcl-xL and Mcl-1, with inhibition constants of 8.9 mmol/L and 3.4 μmol/L, respectively. While compound 1c achieved tight binding affinities to Bcl-xL (Ki = 0.16 μmol/L), has the potential to be a new lead compound.     

3D-QSAR Studies on 3-Substituted N-Benzhydryl-nortropane Analogs as Nociception Receptor Agonists

The nociceptin receptor(NOP) has been involved in multiple biological functions, including pain, anxiety, cough, substance abuse, cardiovascular control, and immunity. Thus, selective NOP agonists might have clinical potential for the treatment of related diseases. In the present work, three-dimensional quantitative structure-activity relationship(3D-QSAR) studies were performed on a series of 3-substituted N-benzhydryl-nortropane analogs as NOP agonists using comparative molecular field analysis(Co MFA) and comparative molecular similarity indices analysis(CoM SIA) techniques. The statistically significant models were obtained with 54 compounds in training set by ligand-based atom-by-atom matching alignment. The CoM FA model gave cross-validated coefficient(q2) value of 0.530 using 6 components, non-cross-validated(r2) value of 0.921 with estimated F value of 93.668, and standard error of estimate(SEE) of 0.185. The best Co MSIA model resulted in q2 = 0.592, r2 = 0.945, N = 10, SEE = 0.162, and F = 75.654, based on steric, electrostatic, hydrophobic and hydrogen bond acceptor fields. The predictive ability of the Co MFA and CoM SIA models was further validated using a test set of 18 molecules that were not included in the training set, which resulted in predictive correlation coefficients(r2pred) of 0.551 and 0.637, respectively. Moreover, the CoM FA and CoM SIA contour maps identified the features important for exhibiting potent binding affinities on NOP, and can thus serve as a useful guide for the design of potential NOP agonists.     

Chaperone Function of Rat Lens α-Crystallin

A solution of γ-crystallin became turbid upon beating at 65 °C for 30 minutes; however, addition of α-crystallin suppressed this thermal aggregation. It was found the effective chaperone function could be achieved with the molar ratio of α/γ greater than 1/20. In terms of crystallin subunit, five molecular α-crystallin subunits could afford chaperone for one molecular γ-crystallin. The gel filtration profile of the sample solution, containing α- and γ-crystallins and preincubation at 65 °C for 30 minutes, showed complex formation between α- and γ-crystallins, indicating α-crystallin was bound to thermally denatured γ-crystallin. A 1-anilinonaphthalene-8-sulfonic acid (ANS) fluorescence study showed that α-crystallin has more hydrophobic regions exposed after thermal incubation. In the presence of urea, both the α-crystallin chaperone activity and the ANS fluorescence intensity decreased. Accordingly, hydrophobic regions of α-crystallin play an indispensible role in its chaperone activity.     

Calculating protein–ligand binding affinities with MMPBSA: Method and error analysis

Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) methods have become widely adopted in estimating protein–ligand binding affinities due to their efficiency and high correlation with experiment. Here different computational alternatives were investigated to assess their impact to the agreement of MMPBSA calculations with experiment. Seven receptor families with both high‐quality crystal structures and binding affinities were selected. First the performance of nonpolar solvation models was studied and it was found that the modern approach that separately models hydrophobic and dispersion interactions dramatically reduces RMSD's of computed relative binding affinities. The numerical setup of the Poisson–Boltzmann methods was analyzed next. The data shows that the impact of grid spacing to the quality of MMPBSA calculations is small: the numerical error at the grid spacing of 0.5 Å is already small enough to be negligible. The impact of different atomic radius sets and different molecular surface definitions was further analyzed and weak influences were found on the agreement with experiment. The influence of solute dielectric constant was also analyzed: a higher dielectric constant generally improves the overall agreement with experiment, especially for highly charged binding pockets. The data also showed that the converged simulations caused slight reduction in the agreement with experiment. Finally the direction of estimating absolute binding free energies was briefly explored. Upon correction of the binding‐induced rearrangement free energy and the binding entropy lost, the errors in absolute binding affinities were also reduced dramatically when the modern nonpolar solvent model was used, although further developments were apparently necessary to further improve the MMPBSA methods. © 2016 Wiley Periodicals, Inc.