Study On The Key Technologies For Predicting Structure Of G Protein-coupled Receptors

G protein-coupled receptors(GPCRs) are the largest superfamily in signal trans-duction proteins of human being. Currently, above1/10of the world’s top200best selling drugs are based on GPCR-related targets. GPCRs are characterized by seven transmembrane a-helices repeatedly crossing the bilayer, leading to extremely difficul-ties for the classical structure determination methods such as NMR and X ray crystal-lography. However, the high-resolution structure of GPCRs is the key to understand the biological functions.Computational prediction of three-dimensional structure of the GPCRs is a promised method, in spite of some challenges due to lacking of priori knowledge, inaccurate ener-gy functions (or objective functions) and search algorithms, and difficulty in modeling the structural topologies of GPCRs. Three key technologies to predict the three-dimensional structure of GPCRs are proposed in this dissertation relying on previ-ous study on predicting structure of protein backbone with parallel ant colonies, and meanwhile the technologies of building an online platform for structure prediction are practiced.The first technique is predicting the lower-layer structural topologies based on se-quence information. This dissertation proposes a sequence-related support vector ma-chine method for predicting β barrel transmembrane regions and a sequence similarity-based method for predicting the deformation angle of helix. Both methods obtain the useful knowledge from sequence information. The computational experiments verified the introduction of sequence information can help to improve the prediction accuracy of the lower layer structural topology.The second technique is modeling the three-dimensional structure of helix bundle based on structural topologies. In this dissertation, a topological model focusing on the characteristics of seven transmembrane helices bundle of GPCRs is established, a four-stage optimization method is proposed based on this model, and topology-based constraints and a new energy item are employed. The computational experiments on three data sets show that this method is able to obtain more accurate structures compared to pGPCR, GPCRDock2010participates and Swiss.The third technique is parallelly modeling the flexible structure of GPCR-ligand complex. Depending on the correlation of GPCRs and ligand, this dissertation pro-poses two optimization protocols for the flexible structure, and then fuses them to the third hybrid protocol(named coREF). Experiments show that coREF has the ability to optimize20conformations submitted by GPCRDock2010participates, and could get lower Ligand-RMSD than RosettaLigand.In addition, this dissertation designs a distributed online structure prediction platform with front nodes, thin nodes and fat nodes. Recently we offer two online services of the protein backbone and side chain structure prediction.The major contributions of this dissertation include three aspects. Firstly, new features are generated by integrating physical and chemical characteristics with se-quence component in the lower-layer structural topology. Secondly, the modeling and sampling technologies based on higher-layer structural topology lead to a new method for predicting helix bundle structure. Moreover, conversion of alternately executing of backbone movements into parallelizing them simultaneously and combination of the ligand independent and dependent protocols for structural flexibility result in a new method for predicting the flexible structure of complex.