Internet resources in GPCR modelling

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Molecular modelling studies on 2-substituted octahydropyrazinopyridoindoles for histamine H2 receptor antagonism

The human histamine H2 receptor (hH2HR) is a G-protein coupled receptor protein with seven transmembrane (TM)-spanning helices primarily involved in regulation of gastric acid secretion. Antagonists targeting hH2HR are useful in the treatment of hyperacidic conditions such as peptic ulcers, gastresophageal reflux disease and gastrointestinal bleeding. We have previously reported the antagonism of 2-substituted pyrazinopyridoindoles at the human histamine H1 receptor and mode of binding of these compounds at the hH1HR using in silico methods. Interestingly, some of the compounds in the series also showed promising activity towards hH2HR that prompted us to investigate the mode of binding of these compounds at hH2HR. In the absence of the crystal structure of hH2HR a homology model has been constructed using multiple sequence alignment, using the X-ray crystal structures of Turkey β1-adrenergic receptor (tβ1AR), Human histamine H1 receptor (hH1HR), Human β2-adrenergic receptor (hβ2AR) and Human D3 dopamine receptor (hD3R). The important residues for binding were depicted in TMIII, TMV, TMVI and TMVII by the homology modelled hH2HR for 2-substituted pyrazinopyridoindoles. A comparative study for deducing the selectivity regarding the binding towards hH1HR and hH2HR has been carried out, which may be useful in designing of selective hH1HR/hH2HR antagonists in these classes of compounds.

Differences in PAR-2 activating potential by king crab (Paralithodes camtschaticus), salmon (Salmo salar), and bovine (Bos taurus) trypsin

BACKGROUND

Salmon trypsin is shown to increase secretion of the pro-inflammatory cytokine interleukin (IL)-8 from human airway epithelial cells through activation of PAR-2. Secretion of IL-8 induced by king crab trypsin is observed in a different concentration range compared to salmon trypsin, and seems to be only partially related to PAR-2 activation. This report aim to identify differences in the molecular structure of king crab trypsin (Paralithodes camtschaticus) compared to salmon (Salmo salar) and bovine trypsin (Bos taurus) that might influence the ability to activate protease-activated receptor-2 (PAR-2).

RESULTS

During purification king crab trypsin displayed stronger binding capacity to the anionic column used in fast protein liquid chromatography compared to fish trypsins, and was identified as a slightly bigger molecule. Measurements of enzymatic activity yielded no obvious differences between the trypsins tested. Molecular modelling showed that king crab trypsin has a large area with strong negative electrostatic potential compared to the smaller negative areas in bovine and salmon trypsins. Bovine and salmon trypsins also displayed areas with strong positive electrostatic potential, a feature lacking in the king crab trypsin. Furthermore we have identified 3 divergent positions (Asp196, Arg244, and Tyr247) located near the substrate binding pocket of king crab trypsin that might affect the binding and cleavage of PAR-2.

CONCLUSION

These preliminary results indicate that electrostatic interactions could be of importance in binding, cleavage and subsequent activation of PAR-2.

Docking studies of novel pyrazinopyridoindoles class of antihistamines with the homology modelled H(1)-receptor

Histamine is an important neurotransmitter as it controls a multitude of physiological functions by activating specific receptors on target cells. It exerts its effects by binding to four different histamine receptors (H(1)-H(4)), which all belong to the large family of G protein-coupled receptors (GPCRs). Research and development of H(1) ligand has largely focused on antagonists which are used for their anti-allergy effects in the periphery. Recent understanding of the clinical importance of H(1) receptors in brain, however, suggests the pharmacotherapeutic potential of H(1) agonists in neurodegenerative and neuropsychiatric disorders. Despite the therapeutic importance of the H(1) receptor, for many years the molecular features of the H(1) receptor protein had been unknown. In view of the recently reported crystal structure of human H(1) receptor and in continuation of our work on 3D-pharmacophore on antihistamine H(1) and homology model of histamine H(1) receptor, docking studies have been carried out on some promising pyrazinopyridoindole class of antihistamine H(1), including two outliers, to validate our earlier reported models/hypotheses on H(1)-receptor, where a good explanation between estimated and observed activities has been obtained. In addition, the docking study also provided insights about the optimal activity of the outliers, for which no explanation was reported previously.

MEDELLER: homology-based coordinate generation for membrane proteins

MOTIVATION

Membrane proteins (MPs) are important drug targets but knowledge of their exact structure is limited to relatively few examples. Existing homology-based structure prediction methods are designed for globular, water-soluble proteins. However, we are now beginning to have enough MP structures to justify the development of a homology-based approach specifically for them.

RESULTS

We present a MP-specific homology-based coordinate generation method, MEDELLER, which is optimized to build highly reliable core models. The method outperforms the popular structure prediction programme Modeller on MPs. The comparison of the two methods was performed on 616 target-template pairs of MPs, which were classified into four test sets by their sequence identity. Across all targets, MEDELLER gave an average backbone root mean square deviation (RMSD) of 2.62 Å versus 3.16 Å for Modeller. On our 'easy' test set, MEDELLER achieves an average accuracy of 0.93 Å backbone RMSD versus 1.56 Å for Modeller.

AVAILABILITY AND IMPLEMENTATION

http://medeller.info; Implemented in Python, Bash and Perl CGI for use on Linux systems; Supplementary data are available at http://www.stats.ox.ac.uk/proteins/resources.