Peroxisome Proliferator-Activated Receptors target family landscape: A chemometrical approach to ligand selectivity based on protein binding site analysis

The Peroxisome Proliferator-Activated Receptors (PPARs) are nuclear receptors which over the last couple of years have been the focus of considerable research efforts aiming to identify compounds with well-defined selectivity profiles for the treatment of metabolic diseases. The ligand binding domains (LBD) of the three known PPAR subtypes exhibit between 60 and 70% sequence identity. To gain insight into the structural determinants of selectivity for the PPAR subtypes, a set of 13 crystal structures of PPAR LBD were classified, using the GRID/CPCA approach. As a result, nearly all of the crystal structures of each different PPAR subtype were found clustered in different regions of the CPCA score plots, and hydrophobic as well as steric interactions were identified as the major determinants of PPAR subtypes selectivity. Furthermore, interpretation of the GRID/CPCA model in structural terms led to the identification of LBD regions which could be targeted to improve the selectivity for a given PPAR subtype. Our findings are consistent with published structure-activity relationships for PPAR ligands as well as with site-directed mutagenesis results.     

Subtype selectivity of dopamine receptor ligands: insights from structure and ligand-based methods

Subtype selective dopamine receptor ligands have long been sought after as therapeutic and/or imaging agents for the treatment and monitoring of neurologic disorders. We report herein on a combined structure- and ligand-based approach to explore the molecular mechanism of the subtype selectivity for a large class of D?-like dopamine receptor ligands (163 ligands in total). Homology models were built for both human D(?L) and D? receptors in complex with haloperidol. Other ligands, which included multiple examples of substituted phenylpiperazines, were aligned against the binding conformations of haloperidol, and three-dimensional quantitative structure activity relationship (3D-QSAR) analyses were carried out. The receptor models show that although D? and D? share highly similar folds and 3D conformations, the slight sequence differences at their extracellular loop regions result in the binding cavity in D? being comparably shallower than in D?, which may explain why some larger ligands bind with greater affinity at D? compared to D? receptors. The QSAR models show excellent correlation and high predictive power even when evaluated by the most stringent criteria. They confirm that the origins of subtype selectivity for the ligands arise primarily due to differences in the contours of the two binding sites. The predictive models suggest that while both steric and electrostatic interactions contribute to the compounds' binding affinity, the major contribution arises from hydrophobic interactions, with hydrogen bonding conferring binding specificity. The current work provides clues for the development of more subtype selective dopamine receptor ligands. Furthermore, it demonstrates the possibility of being able to apply similar modeling methods to other subtypes or classes of receptors to study GPCR receptor-ligand interactions at a molecular level.     

Molecular Alliance—From Orthosteric and Allosteric Ligands to Dualsteric/Bitopic Agonists at G Protein Coupled Receptors

Cell‐membrane‐spanning G protein coupled receptors (GPCRs) belong to the most important therapeutic target structures. Endogenous transmitters bind from the outer side of the membrane to the “orthosteric” binding site either deep in the binding pocket or at the extracellular N‐terminal end of the receptor protein. Exogenous modulators that utilize a different, “allosteric”, binding site unveil a pathway to receptor subtype‐selectivity. However, receptor activation through the orthosteric area is often more powerful. Recently there has been evidence that orthosteric/allosteric, in other words “dualsteric”, hybrid compounds unite subtype selectivity and receptor activation. These “bitopic” modulators channelreceptor activation and subsequent intracellular signaling into a subset of possible routes. This concept offers access to GPCR modulators with an unprecedented receptor‐subtype and signaling selectivity profile and, as a consequence, to drugs with fewer side effects.     

Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators

The estrogen receptor-beta subtype (ERbeta) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERbeta binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q(2) = 0.73 and r(2) = 0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4-7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERbeta modulators having improved affinity.     

3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds

Docking simulation and three-dimensional quantitative structure-activity relationships (3D-QSARs) analyses were conducted on four series of HDAC inhibitors. The studies were performed using the GRID/GOLPE combination using structure-based alignment. Twelve 3-D QSAR models were derived and discussed. Compared to previous studies on similar inhibitors, the present 3-D QSAR investigation proved to be of higher statistical value, displaying for the best global model r2, q2, and cross-validated SDEP values of 0.94, 0.83, and 0.41, respectively. A comparison of the 3-D QSAR maps with the structural features of the binding site showed good correlation. The results of 3D-QSAR and docking studies validated each other and provided insight into the structural requirements for anti-HDAC activity. To our knowledge this is the first 3-D QSAR application on a broad molecular diversity training set of HDACIs.     

Exploring molecular fingerprints of selective PPARδ agonists through comparative and validated chemometric techniques

Peroxysome proliferator-activated receptors (PPARs) have grown greatly in importance due to their role in the metabolic profile. Among three subtypes (α, γ and δ), we here consider the least investigated δ subtype to explore the molecular fingerprints of selective PPARδ agonists. Validated QSAR models (regression based 2D-QSAR, HQSAR and KPLS) and molecular docking with dynamics analyses support the inference of classification-based Bayesian and recursive models. Chemometric studies indicate that the presence of ether linkages and heterocyclic rings has optimum influence in imparting selective bioactivity. Pharmacophore models and docking with molecular dynamics analyses postulate the occurrence of aromatic rings, HB acceptor and a hydrophobic region as crucial molecular fragments for development of PPARδ modulators. Multi-chemometric studies suggest the essential structural requirements of a molecule for imparting potent and selective PPARδ modulation.     

Using a pharmacophore representation concept to elucidate molecular similarity of dopamine antagonists

The pharmacophoric concept plays an important role in ligand-based drug design methods to describe the similarity and diversity of molecules, and could also be exploited as a molecular representation scheme. A three-point pharmacophore method was used as a molecular representation perception. This procedure was implemented for dopamine antagonists of the D(2) receptor subtype. The molecular structures of the antagonists included in this analysis were categorized into two structurally distinct classes. Using structural superposition with internal energy minimization, two pharmacophore models were deduced. Based on these two models other D(2) antagonists that fulfil them were derived and studied. This procedure aided the identification of the common 3D patterns present in diverse molecules that act at the same biological target and the extraction of a common molecular framework for the two structural classes. The pharmacophoric information was found to be suitable for guiding superposition of structurally diverse molecules, using a more biologically meaningful selection of the targeting points.     

3D-QSAR和分子对接研究吲哚咔唑类细胞周期蛋白激酶抑制剂的选择性

细胞周期蛋白激酶(cyclin-dependent kinases, CDKs)是近年来治疗肿瘤的重要靶标. 由于大多数激酶ATP结合位点的保守性, CDK选择性激酶抑制剂的开发成为当前的研发难点和热点. 针对吲哚咔唑类CDK抑制剂, 我们采用比较分子力场分析方法(CoMFA)建立了CDK2-QSAR(quantitative structure-activity relationship)和CDK4-QSSR(quantitative structure-selectivity relationship)模型. 所建模型的交叉验证系数q2分别为0.722和0.703; 非交叉验证系数r2分别为0.977和0.946, 表明其具有较好的预测能力. 同时, 用分子对接的方法分析了这类化合物与CDK4同源模建结构的作用模式, 根据这两个模型发现, 吲哚咔唑类化合物的R5和R6位长链取代对CDK4的选择性具有一定的影响, 而且结合其作用模式比较合理地解释了这类抑制剂的选择性原因, 这对CDKs的选择性研究具有一定的指导意义.     

Full-Atom Model of the Agonist LPS-Bound Toll-like Receptor 4 Dimer in a Membrane Environment

The Toll-like receptor 4 (TLR4)/myeloid differentiation factor 2 (MD-2) innate immunity system is a membrane receptor of paramount importance as therapeutic target. Its assembly, upon binding of Gram-negative bacteria lipopolysaccharide (LPS), and also dependent on the membrane composition, finally triggers the immune response cascade. We have combined ab-initio calculations, molecular docking, all-atom molecular dynamics simulations, and thermodynamics calculations to provide the most realistic and complete 3D models of the active full TLR4 complex embedded into a realistic membrane to date. Our studies give functional and structural insights into the transmembrane domain behavior in different membrane environments, the ectodomain bouncing movement, and the dimerization patterns of the intracellular Toll/Interleukin-1 receptor domain. Our work provides TLR4 models as reasonable 3D structures for the (TLR4/MD-2/LPS)₂ architecture accounting for the active (agonist) state of the TLR4, and pointing to a signal transduction mechanism across cell membrane. These observations unveil relevant molecular aspects involved in the TLR4 innate immune pathways and will promote the discovery of new TLR4 modulators.     

Chemometrical classification of ephrin ligands and Eph kinases using GRID/CPCA approach

Eph receptor tyrosine kinases are divided on two subfamilies based on their affinity for ephrin ligands and play a crucial role in the intercellular processes such as angiogenesis, neurogenesis, and carcinogenesis. As such, Eph kinases represent potential targets for drug design, which requires the knowledge of structural features responsible for their specific interactions. To overcome the existing gap between available sequence and structure information we have built 3D models of eight ephrins and 13 Eph kinase ligand-binding domains using homology modeling techniques. The interaction energies for several molecular probes with binding sites of these models were calculated using GRID and subjected to chemometrical classification based on consensus principal component analysis (CPCA). Despite inherent limitations of the homology models, CPCA was able to successfully distinguish between ephrins and Eph kinases, between Eph kinase subfamilies, and between ephrin subfamilies. As a result we have identified several amino acids that may account for selectivity in ephrin-Eph kinase interactions. In general, although the difference in charge between ephrin and Eph kinase binding domains creates an attractive long-range electrostatic force, the hydrophobic and steric interactions are highly important for the short-range interactions between two proteins. The chemometrical analysis also provides the pharmacophore model, which could be used for virtual screening and de novo ligand design.     

Development of biologically active compounds by combining 3D QSAR and structure-based design methods

One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved – namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It is expected that concepts from receptor-based 3D QSAR will be valuable tools for the analysis of high-throughput screening as well as virtual screening data     

Combined 3D-QSAR modeling and molecular docking study on indolinone derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1

3-Phosphoinositide-dependent protein kinase-1 (PDK1) is a promising target for developing novel anticancer drugs. In order to understand the structure-activity correlation of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The study has resulted in two types of satisfactory 3D-QSAR models, including the CoMFA model (r(2)=0.907; q(2)=0.737) and CoMSIA model (r(2)=0.991; q(2)=0.824), for predicting the biological activity of new compounds. The detailed microscopic structures of PDK1 binding with inhibitors have been studied by molecular docking. We have also developed docking-based 3D-QSAR models (CoMFA with q(2)=0.729; CoMSIA with q(2)=0.79). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures help to better interpret the structure-activity relationship. All of the structural insights obtained from both the 3D-QSAR contour maps and molecular docking are consistent with the available experimental activity data. This is the first report on 3D-QSAR modeling of PDK1 inhibitors. The satisfactory results strongly suggest that the developed 3D-QSAR models and the obtained PDK1-inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.     

INPHARMA-Based Determination of Ligand Binding Modes at α1-Adrenergic Receptors Explains the Molecular Basis of Subtype Selectivity

The structural poses of ligands that bind weakly to protein receptors are challenging to define. In this work we have studied ligand interactions with the adrenoreceptor (AR) subtypes, α_1A-AR and α_1B-AR, which belong to the G protein-coupled receptor (GPCR) superfamily, by employing the solution-based ligand-observed NMR method interligand NOEs for pharmacophore mapping (INPHARMA). A lack of receptor crystal structures and of subtype-selective drugs has hindered the definition of the physiological roles of each subtype and limited drug development. We determined the binding pose of the weakly binding α_1A-AR-selective agonist A-61603 relative to an endogenous agonist, epinephrine, at both α_1A-AR and α_1B-AR. The NMR experimental data were quantitatively compared, by using SpINPHARMA, to the back-calculated spectra based on ligand poses obtained from all-atom molecular dynamics simulations. The results helped mechanistically explain the selectivity of (R)-A-61603 towards α_1A-AR, thus demonstrating an approach for targeting subtype selectivity in ARs.     

HLA-A*0201限制性CTL表位肽的三维定量构效关系的研究

运用比较分子力场(CoMFA)和比较分子相似性指数分析(CoMFA)方法研究了50个HLA-A^*0201限制性CTL表位九肽结构与亲和性间的关系,另外15个表位九肽作为预测集用于检验模型的预测能力.结果表明采用CoMSIA得到的构效关系模型(q^2=0.628,r^2=0.997,F=840.419)要明显优于采用CoMFA得到的构效关系模型.在CoMSIA计算中,当引入疏水场时,三维构效关系模型得到明显改善,通过该三维构效关系模型,可较精确地估算预测集中15个CTL表位肽与HLA-A^*0201间的亲和力(r^2pred=0.743).通过分析分子场等值面图在空间的分布,可以观察到表位肽分子周围的立体及疏水特征对表位肽与HLA-A^*0201间结合亲和力的影响,从而为进一步对CTL表位肽进行结构改造并基于此进行治疗性疫苗分子设计提供理论基础.     

QSAR study and VolSurf characterization of anti-HIV quinolone library

Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.     

Pharmacological characterization of ligands at recombinant NMDA receptor subtypes by electrophysiological recordings and intracellular calcium measurements

Generation of in vitro cellular assays using fluorescence measurements at heterologously expressed NMDA receptors would speed up the process of ligand characterization and enable high-throughput screening. The major drawback to the development of such assays is the cytotoxicity caused by Ca(2+)-flux into the cell via NMDA receptors upon prolonged activation by agonists present in the culture medium. In the present study, we established four cell lines with stable expression of NMDA receptor subtypes NR1/NR2A, NR1/NR2B, NR1/NR2C, or NR1/NR2D in BHK-21 cells. To assess the usefulness of the stable cell lines in conjunction with intracellular calcium ([Ca(2+)](i)) measurements for evaluation of NMDA receptor pharmacology, several ligands were characterized using this method. The results were compared to parallel data obtained by electrophysiological recordings at NMDA receptors expressed in Xenopus oocytes. This comparison showed that agonist potencies determined by [Ca(2+)](i) measurements and electrophysiological recordings correlated well, meaning that the stable cell lines in conjunction with [Ca(2+)](i) measurements provide a useful tool for characterization of NMDA receptor ligands. The agonist series of conformationally constrained glutamate analogues (2S,3R,4S)-alpha-(carboxycyclopropyl)glycine (CCG), 1-aminocyclobutane-r-1,cis-3-dicarboxylic acid (trans-ACBD), and (+/-)-1-aminocyclopentane-r-1,cis-3-dicarboxylic acid (cis-ACPD), as well as the highly potent agonist tetrazolylglycine were among the characterized ligands that were assessed with respect to subtype selectivity at NMDA receptors. However, none of the characterized agonists displays more than 2-3 fold selectivity towards a specific NMDA receptor subtype. Thus, the present study provides a broad pharmacological characterization of structurally diverse ligands at recombinant NMDA receptor subtypes.