The histamine H 4 receptor (H 4 R) is the latest identified histamine receptor to emerge as a potential drug target for inflammatory diseases. Animal models are employed to validate this potential drug target. Concomitantly, various H 4 R orthologs have been cloned, including the human, mouse, rat, guinea pig, monkey, pig, and dog H 4 Rs. In this article, we expressed all these H 4 R orthologs in human embryonic kidney 293T cells and compared their interactions with currently used standard H 4 R ligands, including the H 4 R agonists histamine, 4-methylhistamine, guanidinylethyl isothiourea (VUF 8430), the H 4 R antagonists 1-[(5-chloro-1H-indol-2-yl)carbonyl]-4-methylpiperazine (JNJ 7777120) and [(5-chloro-1H-benzimidazol-2-yl)carbonyl]-4-methylpiperazine (VUF 6002), and the inverse H 4 R agonist thioperamide. Most of the evaluated ligands display significantly different affinities at the different H 4 R orthologs. These "natural mutants" of H 4 R were used to study ligand-receptor interactions by using chimeric human-pig-human and pig-human-pig H 4 R proteins and site-directed mutagenesis. Our results are a useful reference for ligand selection for studies in animal models of diseases and offer new insights in the understanding of H 4 R-ligand receptor interactions.
Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the
actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction
fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine
protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a
method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are
subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These
comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either
proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm.
Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its
application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα).AvailabilityPyPLIF is freely available at http://code.google.com/p/pyplif
The histamine H(4) receptor (H(4)R) is a G protein-coupled receptor (GPCR) that plays an important role in inflammation. Similar to the homologous histamine H(3) receptor (H(3)R), two acidic residues in the H(4)R binding pocket, D(3.32) and E(5.46), act as essential hydrogen bond acceptors of positively ionizable hydrogen bond donors in H(4)R ligands. Given the symmetric distribution of these complementary pharmacophore features in H(4)R and its ligands, different alternative ligand binding mode hypotheses have been proposed. The current study focuses on the elucidation of the molecular determinants of H(4)R-ligand binding modes by combining (3D) quantitative structure-activity relationship (QSAR), protein homology modeling, molecular dynamics simulations, and site-directed mutagenesis studies. We have designed and synthesized a series of clobenpropit (N-(4-chlorobenzyl)-S-[3-(4(5)-imidazolyl)propyl]isothiourea) derivatives to investigate H(4)R-ligand interactions and ligand binding orientations. Interestingly, our studies indicate that clobenpropit (2) itself can bind to H(4)R in two distinct binding modes, while the addition of a cyclohexyl group to the clobenpropit isothiourea moiety allows VUF5228 (5) to adopt only one specific binding mode in the H(4)R binding pocket. Our ligand-steered, experimentally supported protein modeling method gives new insights into ligand recognition by H(4)R and can be used as a general approach to elucidate the structure of protein-ligand complexes.
Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active and inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using molecular fingerprints derived from a unique set of fragment affinity data for the histamine H(3) receptor (H(3)R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H(3)R binders from inactive molecules were successfully used for the identification of new H(3)R ligands. Prospective virtual screening of 156,090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H(3)R ligands that offer new potential starting points for the design of H(3)R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein-fragment interaction space in silico.
Hit optimization of the class of quinazoline containing histamine H(4) receptor (H(4)R) ligands resulted in a sulfonamide substituted analogue with high affinity for the H(4)R. This moiety leads to improved physicochemical properties and is believed to probe a distinct H(4)R binding pocket that was previously identified using pharmacophore modeling. By introducing a variety of sulfonamide substituents, the H(4)R affinity was optimized. The interaction of the new ligands, in combination with a set of previously published quinazoline compounds, was described by a QSAR equation. Pharmacological studies revealed that the sulfonamide analogues have excellent H(4)R affinity and behave as inverse agonists at the human H(4)R. In vivo evaluation of the potent 2-(6-chloro-2-(4-methylpiperazin-1-yl)quinazoline-4-amino)-N-phenylethanesulfonamide (54) (pK(i) = 8.31 +/- 0.10) revealed it to have anti-inflammatory activity in an animal model of acute inflammation.
We have explored the possibilities and challenges of structure-based virtual screening (SBVS) against the human histamine H 4 receptor (H 4 R), a key player in inflammatory responses. Several SBVS strategies, employing different H 4 R ligand conformations, were validated and optimized with respect to their ability to discriminate small fragment-like H 4 R ligands from true inactive fragments, and compared to ligand-based virtual screening (LBVS) approaches. SBVS studies with a molecular interaction fingerprint (IFP) scoring method enabled the identification of H 4 R ligands that were not identified in LBVS runs, demonstrating the scaffold hopping potential of combining molecular docking and IFP scoring. Retrospective VS evaluations against H 4 R homology models based on the histamine H 1 receptor (H 1 R) crystal structure did not give higher enrichments of H 4 R ligands than H 4 R models based on the beta-2 adrenergic receptor (β 2 R). Complementary prospective SBVS studies against β 2 R-based and H 1 R-based H 4 R homology models led to the discovery of different new fragment-like H 4 R ligand chemotypes. Of the 37 tested compounds, 9 fragments (representing 5 different scaffolds) had affinities between 0.14 and 6.3 μM at the H 4 R.
One of well-established biological activities for chalcone derivatives is as acetylcholinesterase inhibitors, which can be developed for the therapy of Alzheimer's disease. Assisted by retrospectively validated structure-based virtual screening (SBVS) protocol to identify potent acetylcholinesterase inhibitors, 80 chalcone derivatives were designed and virtually screened. The F-measure value as the parameter of the predictive ability of the SBVS protocol developed in the research presented in this article was 0.413, which was considerably better than the original SBVS protocol (Fmeasure=0.226). Among the screened chalcone derivatives two were selected as potential lead compounds to design potent inhibitors for acetylcholinesterase: 3-[4-(benzyloxy)-3-methoxyphenyl]-1-(4-hydroxy-3-methoxy-phenyl)prop-2-en-1-one(3k) and 3-[4-(benzyloxy)-3-methoxyphenyl]-1-(4-hydroxyphenyl)prop-2-en-1-one (4k).
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