Improving homology modeling of G-protein coupled receptors through multiple-template derived conserved inter-residue interactions

Evidenced by the three-rounds of G-protein coupled receptors (GPCR) Dock competitions, improving homology modeling methods of helical transmembrane proteins including the GPCRs, based on templates of low sequence identity, remains an eminent challenge. Current approaches addressing this challenge adopt the philosophy of “modeling first, refinement next”. In the present work, we developed an alternative modeling approach through the novel application of available multiple templates. First, conserved inter-residue interactions are derived from each additional template through conservation analysis of each template-target pairwise alignment. Then, these interactions are converted into distance restraints and incorporated in the homology modeling process. This approach was applied to modeling of the human β2 adrenergic receptor using the bovin rhodopsin and the human protease-activated receptor 1 as templates and improved model quality was demonstrated compared to the homology model generated by standard single-template and multiple-template methods. This method of “refined restraints first, modeling next”, provides a fast and complementary way to the current modeling approaches. It allows rational identification and implementation of additional conserved distance restraints extracted from multiple templates and/or experimental data, and has the potential to be applicable to modeling of all helical transmembrane proteins.

[1]  R. Nussinov,et al.  Residue centrality, functionally important residues, and active site shape: Analysis of enzyme and non‐enzyme families , 2006, Protein science : a publication of the Protein Society.

[2]  Ruben Abagyan,et al.  Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. , 2011, Structure.

[3]  Richard R. Neubig,et al.  International Union of Pharmacology. XLVI. G Protein-Coupled Receptor List , 2005, Pharmacological Reviews.

[4]  Chris de Graaf,et al.  Structure of the human glucagon class B G-protein-coupled receptor , 2013, Nature.

[5]  Darrell R. Abernethy,et al.  International Union of Pharmacology: Approaches to the Nomenclature of Voltage-Gated Ion Channels , 2003, Pharmacological Reviews.

[6]  Ruben Abagyan,et al.  GPCR 3D homology models for ligand screening: Lessons learned from blind predictions of adenosine A2a receptor complex , 2010, Proteins.

[7]  Charles L. Brooks,et al.  Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008 , 2009, Nature Reviews Drug Discovery.

[8]  Ali Jazayeri,et al.  Structure of class B GPCR corticotropin-releasing factor receptor 1 , 2013, Nature.

[9]  Zhijun Li,et al.  Developing a high-quality scoring function for membrane protein structures based on specific inter-residue interactions , 2012, Journal of Computer-Aided Molecular Design.

[10]  J. Baldwin,et al.  An alpha-carbon template for the transmembrane helices in the rhodopsin family of G-protein-coupled receptors. , 1997, Journal of molecular biology.

[11]  Giuseppe Tradigo,et al.  Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks , 2014, BMC Bioinformatics.

[12]  Jens Meiler,et al.  Structure of a Class C GPCR Metabotropic Glutamate Receptor 1 Bound to an Allosteric Modulator , 2014, Science.

[13]  Ruben Abagyan,et al.  Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. , 2014, Structure.

[14]  Sebastian Kelm,et al.  MEDELLER: homology-based coordinate generation for membrane proteins , 2010, Bioinform..

[15]  Nagarajan Vaidehi,et al.  Critical analysis of the successes and failures of homology models of G protein‐coupled receptors , 2013, Proteins.

[16]  V. Yarov-Yarovoy,et al.  Structural refinement of the hERG1 pore and voltage‐sensing domains with ROSETTA‐membrane and molecular dynamics simulations , 2010, Proteins.

[17]  Narayanan Eswar,et al.  Protein structure modeling with MODELLER. , 2008, Methods in molecular biology.

[18]  J. Ballesteros,et al.  Structural mimicry in G protein-coupled receptors: implications of the high-resolution structure of rhodopsin for structure-function analysis of rhodopsin-like receptors. , 2001, Molecular pharmacology.

[19]  W. S. Valdar,et al.  Scoring residue conservation , 2002, Proteins.

[20]  Anat Levit,et al.  Homology model-assisted elucidation of binding sites in GPCRs. , 2012, Methods in molecular biology.

[21]  Ravinder Abrol,et al.  Bihelix: Towards de novo structure prediction of an ensemble of G‐protein coupled receptor conformations , 2012, Proteins.

[22]  Arne Elofsson,et al.  Using multiple templates to improve quality of homology models in automated homology modeling , 2008, Protein science : a publication of the Protein Society.

[23]  J. Ballesteros,et al.  [19] Integrated methods for the construction of three-dimensional models and computational probing of structure-function relations in G protein-coupled receptors , 1995 .

[24]  Lars Brive,et al.  Development of 7TM receptor-ligand complex models using ligand-biased, semi-empirical helix-bundle repacking in torsion space: application to the agonist interaction of the human dopamine D2 receptor , 2013, Journal of Computer-Aided Molecular Design.

[25]  Marcin J. Skwark,et al.  PconsFold: improved contact predictions improve protein models , 2014, Bioinform..

[26]  Bryan L. Roth,et al.  Structure of the human smoothened receptor bound to an antitumour agent , 2013, Nature.

[27]  John P. Overington,et al.  How many drug targets are there? , 2006, Nature Reviews Drug Discovery.

[28]  Laura López,et al.  Progress in the structural prediction of G protein‐coupled receptors: D3 receptor in complex with eticlopride , 2011, Proteins.

[29]  R. Stevens,et al.  FoldGPCR: Structure prediction protocol for the transmembrane domain of G protein‐coupled receptors from class A , 2010, Proteins.

[30]  Dániel Kozma,et al.  PDBTM: Protein Data Bank of transmembrane proteins after 8 years , 2012, Nucleic Acids Res..

[31]  Yang Zhang,et al.  High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling , 2013, Bioinform..

[32]  M. Babu,et al.  Molecular signatures of G-protein-coupled receptors , 2013, Nature.

[33]  Ceslovas Venclovas,et al.  Comparative modeling in CASP5: Progress is evident, but alignment errors remain a significant hindrance , 2003, Proteins.

[34]  Björn Wallner,et al.  Model quality assessment for membrane proteins , 2010, Bioinform..

[35]  David Baker,et al.  High-Resolution Modeling of Transmembrane Helical Protein Structures from Distant Homologues , 2014, PLoS Comput. Biol..

[36]  Michael Lappe,et al.  CMView: Interactive contact map visualization and analysis , 2011, Bioinform..

[37]  Hsi-Hsien Lin G-protein-Coupled Receptors and Their (Bio) Chemical Significance Win 2012 Nobel Prize in Chemistry , 2013, Biomedical journal.

[38]  C.-M. Chen,et al.  Computational prediction of kink properties of helices in membrane proteins , 2014, Journal of Computer-Aided Molecular Design.

[39]  Helgi B. Schiöth,et al.  Structural diversity of G protein-coupled receptors and significance for drug discovery , 2008, Nature Reviews Drug Discovery.

[40]  P. Conn Methods in neurosciences , 1991 .

[41]  Marcin J. Skwark,et al.  PconsFold: improved contact predictions improve protein models , 2014, Bioinform..