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      patGPCR: A Multitemplate Approach for Improving 3D Structure Prediction of Transmembrane Helices of G-Protein-Coupled Receptors

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          The structures of the seven transmembrane helices of G-protein-coupled receptors are critically involved in many aspects of these receptors, such as receptor stability, ligand docking, and molecular function. Most of the previous multitemplate approaches have built a “super” template with very little merging of aligned fragments from different templates. Here, we present a parallelized multitemplate approach, patGPCR, to predict the 3D structures of transmembrane helices of G-protein-coupled receptors. patGPCR, which employs a bundle-packing related energy function that extends on the RosettaMem energy, parallelizes eight pipelines for transmembrane helix refinement and exchanges the optimized helix structures from multiple templates. We have investigated the performance of patGPCR on a test set containing eight determined G-protein-coupled receptors. The results indicate that patGPCR improves the TM RMSD of the predicted models by 33.64% on average against a single-template method. Compared with other homology approaches, the best models for five of the eight targets built by patGPCR had a lower TM RMSD than that obtained from SWISS-MODEL; patGPCR also showed lower average TM RMSD than single-template and multiple-template MODELLER.

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          Most cited references 46

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          Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

          We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at Copyright 2001 Academic Press.
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            Comparative protein modelling by satisfaction of spatial restraints.

             A. Sali,  T L Blundell (1993)
            We describe a comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equivalent residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain C alpha-C alpha distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the molecular pdf such that the model violates the input restraints as little as possible. The molecular pdf is derived as a combination of pdfs restraining individual spatial features of the whole molecule. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modelling of trypsin from two other serine proteinases.
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              High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor.

              Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors constitute the largest family of eukaryotic signal transduction proteins that communicate across the membrane. We report the crystal structure of a human beta2-adrenergic receptor-T4 lysozyme fusion protein bound to the partial inverse agonist carazolol at 2.4 angstrom resolution. The structure provides a high-resolution view of a human G protein-coupled receptor bound to a diffusible ligand. Ligand-binding site accessibility is enabled by the second extracellular loop, which is held out of the binding cavity by a pair of closely spaced disulfide bridges and a short helical segment within the loop. Cholesterol, a necessary component for crystallization, mediates an intriguing parallel association of receptor molecules in the crystal lattice. Although the location of carazolol in the beta2-adrenergic receptor is very similar to that of retinal in rhodopsin, structural differences in the ligand-binding site and other regions highlight the challenges in using rhodopsin as a template model for this large receptor family.

                Author and article information

                Comput Math Methods Med
                Comput Math Methods Med
                Computational and Mathematical Methods in Medicine
                Hindawi Publishing Corporation
                11 March 2013
                : 2013
                1School of Computer Science and Technology, Soochow University, Suzhou 215006, China
                2Jiangsu Provincial Key Lab for Information Processing Technologies, Suzhou 215006, China
                3School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
                Author notes

                Academic Editor: Hong-Bin Shen

                Copyright © 2013 Hongjie Wu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article

                Applied mathematics


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