Conformational memories with variable bond angles

Conformational Memories (CM) is a simulated annealing/Monte Carlo method that explores peptide and protein dihedral conformational space completely and efficiently, independent of the original conformation. Here we extend the CM method to include the variation of a randomly chosen bond angle, in addition to the standard variation of two or three randomly chosen dihedral angles, in each Monte Carlo trial of the CM exploratory and biased phases. We test the hypothesis that the inclusion of variable bond angles in CM leads to an improved sampling of conformational space. We compare the results with variable bond angles to CM with no bond angle variation for the following systems: (1) the pentapeptide Met‐enkephalin, which is a standard test case for conformational search methods; (2) the proline ring pucker in a 17mer model peptide, (Ala)8Pro(Ala)8; and (3) the conformations of the Ser 7.39 χ1 in transmembrane helix 7 (TMH7) of the cannabinoid CB1 receptor, a 25‐residue system. In each case, analysis of the CM results shows that the inclusion of variable bond angles results in sampling of regions of conformational space that are inaccessible to CM calculations with only variable dihedral angles, and/or a shift in conformational populations from those calculated when variable bond angles are not included. The incorporation of variable bond angles leads to an improved sampling of conformational space without loss of efficiency. Our examples show that this improved sampling leads to better exploration of biologically relevant conformations that have been experimentally validated. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008

[1]  David J. Wales,et al.  The free energy landscape and dynamics of met-enkephalin , 2003 .

[2]  A. Schug,et al.  Energy landscape paving simulations of the trp-cage protein. , 2005, The Journal of chemical physics.

[3]  Seokmin Shin,et al.  Folding simulations with novel conformational search method. , 2007, The Journal of chemical physics.

[4]  U H Hansmann,et al.  The folding funnel landscape for the peptide met‐enkephalin , 1998, Proteins.

[5]  M. Troyer,et al.  Optimized parallel tempering simulations of proteins. , 2006, The Journal of chemical physics.

[6]  J. Ballesteros,et al.  Agonist alkyl tail interaction with cannabinoid CB1 receptor V6.43/I6.46 groove induces a helix 6 active conformation , 2002 .

[7]  E. Knapp,et al.  An energy function for dynamics simulations of polypeptides in torsion angle space , 1998 .

[8]  K. Konvička,et al.  A proposed structure for transmembrane segment 7 of G protein-coupled receptors incorporating an asn-Pro/Asp-Pro motif. , 1998, Biophysical journal.

[9]  K. Dill,et al.  The flexibility in the proline ring couples to the protein backbone , 2005, Protein science : a publication of the Protein Society.

[10]  Leonardo Pardo,et al.  Serine and Threonine Residues Bend α-Helices in the χ1 = g− Conformation , 2000 .

[11]  W. DeGrado,et al.  Polar side chains drive the association of model transmembrane peptides. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[12]  K. Freed,et al.  Long time dynamics of Met-enkephalin: comparison of explicit and implicit solvent models. , 2002, Biophysical journal.

[13]  Peter S. Shenkin,et al.  Cluster analysis of molecular conformations , 1994, J. Comput. Chem..

[14]  R. Berry,et al.  Investigations into sequence and conformational dependence of backbone entropy, inter-basin dynamics and the Flory isolated-pair hypothesis for peptides. , 2003, Journal of molecular biology.

[15]  Daniel Fleischer,et al.  Mutation Studies of Ser7.39 and Ser2.60 in the Human CB1 Cannabinoid Receptor: Evidence for a Serine-Induced Bend in CB1 Transmembrane Helix 7 , 2007, Molecular Pharmacology.

[16]  Alexander D. MacKerell,et al.  Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations , 2004, J. Comput. Chem..

[17]  Global minimum configuration of a small peptide for the ECEPP/2 and ECEPP/3 force fields , 1997 .

[18]  W. F. van Gunsteren,et al.  Effect of constraints on the dynamics of macromolecules , 1982 .

[19]  R. Osman,et al.  Role of the extracellular loops of the thyrotropin-releasing hormone receptor: evidence for an initial interaction with thyrotropin-releasing hormone. , 1997, Biochemistry.

[20]  M. Betancourt,et al.  Efficient Monte Carlo trial moves for polypeptide simulations. , 2005, The Journal of chemical physics.

[21]  U. Hansmann.  EFFECTIVE WAY FOR DETERMINATION OF MULTICANONICAL WEIGHTS , 1997 .

[22]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[23]  A Mitsutake,et al.  Generalized-ensemble algorithms for molecular simulations of biopolymers. , 2000, Biopolymers.

[24]  Harold A. Scheraga,et al.  Structure and free energy of complex thermodynamic systems , 1988 .

[25]  F Guarnieri,et al.  Exploration of biologically relevant conformations of anandamide, 2-arachidonylglycerol, and their analogues using conformational memories. , 1998, Journal of medicinal chemistry.

[26]  Patricia H. Reggio,et al.  Computational methods in drug design: Modeling G protein-coupled receptor monomers, dimers, and oligomers , 2006, The AAPS Journal.

[27]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[28]  B. Matthews,et al.  Intrahelical hydrogen bonding of serine, threonine and cysteine residues within alpha-helices and its relevance to membrane-bound proteins. , 1984, Journal of molecular biology.

[29]  Robert P. Millar,et al.  Functional Microdomains in G-protein-coupled Receptors , 1998, The Journal of Biological Chemistry.

[30]  U H Hansmann,et al.  Characteristic temperatures of folding of a small peptide. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Yuko Okamoto,et al.  Prediction of peptide conformation by multicanonical algorithm: New approach to the multiple‐minima problem , 1993, J. Comput. Chem..

[32]  William L. Jorgensen,et al.  Monte Carlo backbone sampling for polypeptides with variable bond angles and dihedral angles using concerted rotations and a Gaussian bias , 2003 .

[33]  H. Scheraga,et al.  Monte Carlo-minimization approach to the multiple-minima problem in protein folding. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Marvin C. Gershengorn,et al.  Interactions between Conserved Residues in Transmembrane Helices 1, 2, and 7 of the Thyrotropin-releasing Hormone Receptor* , 1997, The Journal of Biological Chemistry.

[35]  H. Weinstein,et al.  Conformational Memories and the Exploration of Biologically Relevant Peptide Conformations: An Illustration for the Gonadotropin-Releasing Hormone , 1996 .

[36]  Y. Okamoto,et al.  Numerical comparisons of three recently proposed algorithms in the protein folding problem , 1997 .

[37]  D. Hurst,et al.  Cysteine 2.59(89) in the Second Transmembrane Domain of Human CB2 Receptor Is Accessible within the Ligand Binding Crevice: Evidence for Possible CB2 Deviation from a Rhodopsin Template , 2005, Molecular Pharmacology.

[38]  R. Osman,et al.  Restricted analogues provide evidence of a biologically active conformation of thyrotropin-releasing hormone. , 1996, Molecular pharmacology.

[39]  R. Iyengar,et al.  A surface on the G protein beta-subunit involved in interactions with adenylyl cyclases. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[40]  M. Karplus,et al.  Simulation of activation free energies in molecular systems , 1996 .

[41]  W. L. Jorgensen,et al.  Polypeptide folding using Monte Carlo sampling, concerted rotation, and continuum solvation. , 2004, Journal of the American Chemical Society.

[42]  S. McAllister,et al.  Structural Mimicry in Class A G Protein-coupled Receptor Rotamer Toggle Switches , 2004, Journal of Biological Chemistry.

[43]  U. Hansmann.  Generalized ensemble techniques and protein folding simulations , 2002 .

[44]  Yuko Okamoto,et al.  Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. , 2003, Journal of molecular graphics & modelling.

[45]  Bernd A. Berg,et al.  Generalized ensemble simulations for complex systems , 2001, cond-mat/0110521.

[46]  J. Ballesteros,et al.  Beta2 adrenergic receptor activation. Modulation of the proline kink in transmembrane 6 by a rotamer toggle switch. , 2002, The Journal of biological chemistry.

[47]  Frank Guarnieri,et al.  Conformational memories and a simulated annealing program that learns: Application to LTB4 , 1995, J. Comput. Chem..

[48]  M. Ratner,et al.  Monte Carlo methods for short polypeptides , 1998 .

[49]  F Guarnieri,et al.  Activation of the cannabinoid CB1 receptor may involve a W6 48/F3 36 rotamer toggle switch. , 2002, The journal of peptide research : official journal of the American Peptide Society.

[50]  Robert E. Bruccoleri,et al.  Chain closure with bond angle variations , 1985 .