The h-P2X3 glycoprotein receptor as an example of integrating bioinformatics and structural research

This review deals with the molecular modelling of a subtype of the membrane-embedded purinoreceptor P2X family, which belongs to the large class of membrane-embedded glycoproteins. The P2X family has two transmembrane domains and a core of five extracellularly occurring disulfide bonds. At present, seven different P2X receptor subtypes (P2X1 - X7) have been cloned. The human purinoreceptor P2X3 (h-P2X3) is a putative drug target for the development of inhibitors against chronic inflammatory, neuropathic and mixed-pain conditions. No details on P2X receptor architecture are known at the atomic resolution level by X-ray or NMR analyses. An attempt was made to predict the conformation of h-P2X3 using homology based comparative modelling and threading, but the modelling could not be carried out due to missing template proteins. State-of-the-art ab initio protein structure prediction methods also failed. A novel approach has been applied and exemplified on the h-P2X3 receptor. The coordinates of the secondary structure of h-P2X3 were determined by a profile-based neural network prediction. The conformation was geometry optimised using the quantum chemistry RHF/3-21G minimal basic set and all-atom molecular mechanics AMBER force field. A dielectric constant of ε = 3.5 was used to simulate the lipophilic environment of the membrane-bound protein. The h-P2X3 protein has a number of interacting peptide modules. An example is an extracellularly occurring triad of sterically interacting domains, which consists of a nucleotide binding domain (amino acids in positions 62 - 66), a PKC phosphorylation site (196 - 198) and a N-glycosylation attachment site (194 - 197). The discovery of this peptide module, and of other interacting modules, raises the possibility of exploiting structure-based strategies to design P2X3 inhibitors. Nevertheless, it should be noted that the predicted structures are defined in a probabilistic sense. Only biological and chemical knowledge can determine whether or not these predictions are meaningful. Thus, the results from the computational tools are probabilistic predictions and subject to further experimental verification.