Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpha (hERalpha) and beta (hERbeta). Because the levels and relative proportion of hERalpha and hERbeta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hERalpha and hERbeta. Significant statistical coefficients were obtained (hERalpha, q(2) = 0.76; hERbeta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hERalpha and hERbeta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design of novel hER modulators with improved selectivity.     

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.     

GRID: A high‐resolution protein structure refinement algorithm

The energy‐based refinement of protein structures generated by fold prediction algorithms to atomic‐level accuracy remains a major challenge in structural biology. Energy‐based refinement is mainly dependent on two components: (1) sufficiently accurate force fields, and (2) efficient conformational space search algorithms. Focusing on the latter, we developed a high‐resolution refinement algorithm called GRID. It takes a three‐dimensional protein structure as input and, using an all‐atom force field, attempts to improve the energy of the structure by systematically perturbing backbone dihedrals and side‐chain rotamer conformations. We compare GRID to Backrub, a stochastic algorithm that has been shown to predict a significant fraction of the conformational changes that occur with point mutations. We applied GRID and Backrub to 10 high‐resolution (≤ 2.8 Å) crystal structures from the Protein Data Bank and measured the energy improvements obtained and the computation times required to achieve them. GRID resulted in energy improvements that were significantly better than those attained by Backrub while expending about the same amount of computational resources. GRID resulted in relaxed structures that had slightly higher backbone RMSDs compared to Backrub relative to the starting crystal structures. The average RMSD was 0.25 ± 0.02 Å for GRID versus 0.14 ± 0.04 Å for Backrub. These relatively minor deviations indicate that both algorithms generate structures that retain their original topologies, as expected given the nature of the algorithms. © 2012 Wiley Periodicals, Inc.     

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.     

Exploration of enzyme-ligand interactions in CYP2D6 & 3A4 homology models and crystal structures using a novel computational approach

New crystal structures of human CYP2D6 and CYP3A4 have recently been reported, and in this study, we wanted to compare them with previously used homology models with respect to predictions of site of metabolism and ligand-enzyme interactions. The data set consisted of a family of synthetic opioid analgesics with the aim to cover both CYP2D6 and CYP3A4, as most of these compounds are metabolized by both isoforms. The program MetaSite was used for the site of metabolism predictions, and the results were validated by experimental assessment of the major metabolites formed with recombinant CYP450s. This was made on a selection of 14 compounds in the data set. The prediction rates for MetaSite were 79-100% except for the CYP3A4 homology model, which picked the correct site in half of the cases. Despite differences in orientation of some important amino acids in the active sites, the MetaSite-predicted sites were the same for the different structures, with the exception of the CYP3A4 homology model. Further exploration of interactions with ligands was done by docking substrates/inhibitors in the different structures with the docking program GLUE. To address the challenge in interpreting patterns of enzyme-ligand interactions for the large number of different docking poses, a new computational tool to handle the results from the dockings was developed, in which the output highlights the relative importance of amino acids in CYP450-substrate/inhibitor interactions. The method is based on calculations of the interaction energies for each pose with the surrounding amino acids. For the CYP3A4 structures, this method was compared with consensus principal component analysis (CPCA), a commonly used method for structural comparison to evaluate the usefulness of the new method. The results from the two methods were comparable with each other, and the highlighted amino acids resemble those that were identified to have a different orientation in the compared structures. The new method has clear advantages over CPCA in that it is far simpler to interpret and there is no need for protein alignment. The methodology enables structural comparison but also gives insights on important amino acid substrate/inhibitor interactions and can therefore be very useful when suggesting modifications of new chemical entities to improve their metabolic profiles.     

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.     

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.     

Determinants for α4β2 vs. α3β4 Subtype Selectivity of Pyrrolidine-Based nAChRs Ligands: A Computational Perspective with Focus on Recent cryo-EM Receptor Structures

The selectivity of α4β2 nAChR agonists over the α3β4 nicotinic receptor subtype, predominant in ganglia, primarily conditions their therapeutic range and it is still a complex and challenging issue for medicinal chemists and pharmacologists. Here, we investigate the determinants for such subtype selectivity in a series of more than forty α4β2 ligands we have previously reported, docking them into the structures of the two human subtypes, recently determined by cryo-electron microscopy. They are all pyrrolidine based analogues of the well-known α4β2 agonist N-methylprolinol pyridyl ether A-84543 and differ in the flexibility and pattern substitution of their aromatic portion. Indeed, the direct or water mediated interaction with hydrophilic residues of the relatively narrower β2 minus side through the elements decorating the aromatic ring and the stabilization of the latter by facing to the not conserved β2-Phe119 result as key distinctive features for the α4β2 affinity. Consistently, these compounds show, despite the structural similarity, very different α4β2 vs. α3β4 selectivities, from modest to very high, which relate to rigidity/extensibility degree of the portion containing the aromatic ring and to substitutions at the latter. Furthermore, the structural rationalization of the rat vs. human differences of α4β2 vs. α3β4 selectivity ratios is here proposed.     

An integrated approach to ligand- and structure-based drug design: development and application to a series of serine protease inhibitors

A novel approach was developed to rationally interface structure- and ligand-based drug design through the rescoring of docking poses and automated generation of molecular alignments for 3D quantitative structure-activity relationship investigations. The procedure was driven by a genetic algorithm optimizing the value of a novel fitness function, accounting simultaneously for best regressions among binding-energy docking scores and affinities and for minimal geometric deviations from properly established crystal-based binding geometry. The GRID/CPCA method, as implemented in GOLPE, was used to feature molecular determinants of ligand binding affinity for each molecular alignment. In addition, unlike standard procedures, a novel multipoint equation was adopted to predict the binding affinity of ligands in the prediction set. Selectivity was investigated through square plots reporting experimental versus recalculated binding affinities on the targets under examination. The application of our approach to the modeling of affinity data of a large series of 3-amidinophenylalanine inhibitors of thrombin, trypsin, and factor Xa generated easily interpretable and independent models with robust statistics. As a further validation study, our approach was successfully applied to a series of 3,4,7-substituted coumarins, acting as selective MAO-B inhibitors.     

Core and peripheral connectivity based cluster analysis over PPI network

A number of methods have been proposed in the literature of protein–protein interaction (PPI) network analysis for detection of clusters in the network. Clusters are identified by these methods using various graph theoretic criteria. Most of these methods have been found time consuming due to involvement of preprocessing and post processing tasks. In addition, they do not achieve high precision and recall consistently and simultaneously. Moreover, the existing methods do not employ the idea of core-periphery structural pattern of protein complexes effectively to extract clusters. In this paper, we introduce a clustering method named CPCA based on a recent observation by researchers that a protein complex in a PPI network is arranged as a relatively dense core region and additional proteins weakly connected to the core. CPCA uses two connectivity criterion functions to identify core and peripheral regions of the cluster. To locate initial node of a cluster we introduce a measure called DNQ (Degree based Neighborhood Qualification) index that evaluates tendency of the node to be part of a cluster. CPCA performs well when compared with well-known counterparts. Along with protein complex gold standards, a co-localization dataset has also been used for validation of the results.     

Theoretical studies on the structural rearrangement of ligand binding pocket in human vitamin D receptor

The structural rearrangement of the ligand binding domain (LBD) of human Vitamin D receptor (hVDR) complexed with 1α, 25‐dihydroxyvitamin D₃ (natural ligand) and its analogues (denoted as b and c ) was studied by molecular dynamics (MD) simulations. MD simulations revealed that these ligands could induce different structural changes of LBD, in which 1α, 25‐dihydroxyvitamin D₃ only led to a minute change, suggesting that LBD adopted its canonical active conformation upon binding the natural ligand, while b and c could provoke a clear structural rearrangement of the LBD. In complex of hVDR‐LBD/ b , it is found that helix 6 (H6) and subsequent loop 6‐7 shift outward and the last turn of H11 shifts away from H12, which generate a new cavity at the bottom of binding pocket to accommodate the extra butyl group on the side chain of ligand b . As for hVDR‐LBD/ c , the steric exclusion of the second side chain of ligand c makes the N‐terminal of H7 move outsides and C‐terminal of H11 close to H12, expanding the bottom of the pocket. These calculation results agree well the experimental observations. © 2010 Wiley Periodicals, Inc. Int J Quantum Chem, 2011     

Structure-based prediction of subtype selectivity of histamine H3 receptor selective antagonists in clinical trials

Histamine receptors (HRs) are excellent drug targets for the treatment of diseases, such as schizophrenia, psychosis, depression, migraine, allergies, asthma, ulcers, and hypertension. Among them, the human H(3) histamine receptor (hH(3)HR) antagonists have been proposed for specific therapeutic applications, including treatment of Alzheimer's disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity. However, many of these drug candidates cause undesired side effects through the cross-reactivity with other histamine receptor subtypes. In order to develop improved selectivity and activity for such treatments, it would be useful to have the three-dimensional structures for all four HRs. We report here the predicted structures of four HR subtypes (H(1), H(2), H(3), and H(4)) using the GEnSeMBLE (GPCR ensemble of structures in membrane bilayer environment) Monte Carlo protocol, sampling ～35 million combinations of helix packings to predict the 10 most stable packings for each of the four subtypes. Then we used these 10 best protein structures with the DarwinDock Monte Carlo protocol to sample ～50?000 × 10(20) poses to predict the optimum ligand-protein structures for various agonists and antagonists. We find that E206(5.46) contributes most in binding H(3) selective agonists (5, 6, 7) in agreement with experimental mutation studies. We also find that conserved E5.46/S5.43 in both of hH(3)HR and hH(4)HR are involved in H(3)/ H(4) subtype selectivity. In addition, we find that M378(6.55) in hH(3)HR provides additional hydrophobic interactions different from hH(4)HR (the corresponding amino acid of T323(6.55) in hH(4)HR) to provide additional subtype bias. From these studies, we developed a pharmacophore model based on our predictions for known hH(3)HR selective antagonists in clinical study [ABT-239 1, GSK-189,254 2, PF-3654746 3, and BF2.649 (tiprolisant) 4] that suggests critical selectivity directing elements are: the basic proton interacting with D114(3.32), the spacer, the aromatic ring substituted with the hydrophilic or lipophilic groups interacting with lipophilic pockets in transmembranes (TMs) 3-5-6 and the aliphatic ring located in TMs 2-3-7. These 3D structures for all four HRs should help guide the rational design of novel drugs for the subtype selective antagonists and agonists with reduced side effects.     

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.     

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.     

蛋白质相互作用: 界面分析, 结合自由能计算与相互作用设计

蛋白质相互作用在生命活动中起着重要作用. 研究蛋白质间相互作用的本质有助于了解生命活动中这些基本单元的作用. 本文主要综述了近期蛋白质相互作用研究的进展, 包括蛋白质相互作用界面的基本性质, 蛋白质结合自由能的计算方法, 不同相互作用在蛋白质结合/解离中的角色和差异, 以及上述知识在蛋白质相互作用设计中的应用. 蛋白质相互作用界面的特性, 例如界面大小、保守性以及结构的动态性质, 使得具有生物功能的蛋白质相互作用界面区别于非特异性的晶体堆积界面. 生物功能界面的一个重要结构特征是界面上存在着关键残基以及相对独立的相互作用模块. 利用多种方法, 如MM-PBSA、统计平均势以及不同的相互作用自由能模型, 可以在不同的精度上计算蛋白质相互作用自由能. 利用蛋白质相互作用界面的特点, 从不同的角度进行蛋白质相互作用对的设计与改造, 近年来已经有了不少成功的例子, 但还存在着很大的挑战. 我们认为在今后的蛋白质相互作用设计中, 考虑各种因素对蛋白质结合与解离的动力学过程的影响将有助于提高人类控制蛋白质相互作用的能力.     

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.