Skip to main content
SpringerLink
Log in

Balancing Bond, Nonbond, and Gō-Like Terms in Coarse Grain Simulations of Conformational Dynamics

  • Protocol
  • First Online:
Protein Dynamics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1084))

Abstract

Characterization of the protein conformational landscape remains a challenging problem, whether it concerns elucidating folding mechanisms, predicting native structures or modeling functional transitions. Coarse-grained molecular dynamics simulation methods enable exhaustive sampling of the energetic landscape at resolutions of biological interest. The general utility of structure-based models is reviewed along with their differing levels of approximation. Simple Gō models incorporate attractive native interactions and repulsive nonnative contacts, resulting in an ideal smooth landscape. Non-Gō coarse-grained models reduce the parameter set as needed but do not include bias to any desired native structure. While non-Gō models have achieved limited success in protein coarse-graining, they can be combined with native structured-based potentials to create a balanced and powerful force field. Recent applications of such Gō-like models have yielded insight into complex folding mechanisms and conformational transitions in large macromolecules. The accuracy and usefulness of reduced representations are also revealed to be a function of the mathematical treatment of the intrinsic bonded topology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Taketomi H, Ueda Y, Go N (1975) Studies on protein folding, unfolding and fluctuations by computer simulation. 1. Effect of specific amino acid sequence represented by specific inter-unit interactions. Int J Pept Protein Res 7:445–459

    Article  PubMed  CAS  Google Scholar 

  2. Bryngelson JD, Onuchic JN, Socci ND et al (1995) Funnels, pathways, and the energy landscape of protein folding: a synthesis. Proteins 21:167–195

    Article  PubMed  CAS  Google Scholar 

  3. Dill KA, Chan HS (1997) From Levinthal to pathways to funnels. Nat Struct Biol 4:10–19

    Article  PubMed  CAS  Google Scholar 

  4. Noel JK, Onuchic JN (2012) The many faces of structure-based potentials: from protein folding landscapes to structural characterization of complex biomolecules. In: Dokholyan NV (ed) Computational modeling of biological systems. Springer, New York, NY, pp 31–54

    Chapter  Google Scholar 

  5. Hills RD Jr, Brooks CL III (2009) Insights from coarse-grained Go models for protein folding and dynamics. Int J Mol Sci 10:889–905

    Article  PubMed  CAS  Google Scholar 

  6. Takada S (2012) Coarse-grained molecular simulations of large biomolecules. Curr Opin Struct Biol 22:130–137

    Article  PubMed  CAS  Google Scholar 

  7. Adelman JL, Dale AL, Zwier MC et al (2011) Simulations of the alternating access mechanism of the sodium symporter Mhp1. Biophys J 101:2399–2407

    Article  PubMed  CAS  Google Scholar 

  8. Best RB, Chen YG, Hummer G (2005) Slow protein conformational dynamics from multiple experimental structures: the helix/sheet transition of arc repressor. Structure 13:1755–1763

    Article  PubMed  CAS  Google Scholar 

  9. Daily MD, Phillips GN Jr, Cui Q (2011) Interconversion of functional motions between mesophilic and thermophilic adenylate kinases. PLoS Comput Biol 7:e1002103

    Article  PubMed  CAS  Google Scholar 

  10. Grubisic I, Shokhirev MN, Orzechowski M et al (2010) Biased coarse-grained molecular dynamics simulation approach for flexible fitting of X-ray structure into cryo electron microscopy maps. J Struct Biol 169:95–105

    Article  PubMed  CAS  Google Scholar 

  11. Hyeon C, Jennings PA, Adams JA et al (2009) Ligand-induced global transitions in the catalytic domain of protein kinase A. Proc Natl Acad Sci USA 106:3023–3028

    Article  PubMed  CAS  Google Scholar 

  12. Lammert H, Schug A, Onuchic JN (2009) Robustness and generalization of structure-based models for protein folding and function. Proteins 77:881–891

    Article  PubMed  CAS  Google Scholar 

  13. Okazaki K, Koga N, Takada S et al (2006) Multiple-basin energy landscapes for large-amplitude conformational motions of proteins: structure-based molecular dynamics simulations. Proc Natl Acad Sci USA 103:11844–11849

    Article  PubMed  CAS  Google Scholar 

  14. Ratje AH, Loerke J, Mikolajka A et al (2010) Head swivel on the ribosome facilitates translocation by means of intra-subunit tRNA hybrid sites. Nature 468:713–716

    Article  PubMed  CAS  Google Scholar 

  15. Wang Y, Tang C, Wang E et al (2012) Exploration of multi-state conformational dynamics and underlying global functional landscape of maltose binding protein. PLoS Comput Biol 8:e1002471

    Article  PubMed  CAS  Google Scholar 

  16. Brown S, Fawzi NJ, Head-Gordon T (2003) Coarse-grained sequences for protein folding and design. Proc Natl Acad Sci USA 100:10712–10717

    Article  PubMed  CAS  Google Scholar 

  17. Favrin G, Irback A, Wallin S (2002) Folding of a small helical protein using hydrogen bonds and hydrophobicity forces. Proteins 47:99–105

    Article  PubMed  CAS  Google Scholar 

  18. Honeycutt JD, Thirumalai D (1990) Metastability of the folded states of globular proteins. Proc Natl Acad Sci USA 87:3526–3529

    Article  PubMed  CAS  Google Scholar 

  19. Irback A, Sjunnesson F, Wallin S (2000) Three-helix-bundle protein in a Ramachandran model. Proc Natl Acad Sci USA 97:13614–13618

    Article  PubMed  CAS  Google Scholar 

  20. Miller MA, Wales DJ (1999) Energy landscape of a model protein. J Chem Phys 111:6610–6616

    Article  CAS  Google Scholar 

  21. Takada S, Luthey-Schulten Z, Wolynes PG (1999) Folding dynamics with nonadditive forces: a simulation study of a designed helical protein and a random heteropolymer. J Chem Phys 110:11616–11629

    Article  CAS  Google Scholar 

  22. Karanicolas J, Brooks CL III (2002) The origins of asymmetry in the folding transition states of protein L and protein G. Protein Sci 11:2351–2361

    Article  PubMed  CAS  Google Scholar 

  23. Yue K, Fiebig KM, Thomas PD et al (1995) A test of lattice protein folding algorithms. Proc Natl Acad Sci USA 92:325–329

    Article  PubMed  CAS  Google Scholar 

  24. Oakley MT, Wales DJ, Johnston RL (2011) Energy landscape and global optimization for a frustrated model protein. J Phys Chem B 115:11525–11529

    Article  PubMed  CAS  Google Scholar 

  25. Clementi C, Nymeyer H, Onuchic JN (2000) Topological and energetic factors: what determines the structural details of the transition state ensemble and “en-route” intermediates for protein folding? An investigation for small globular proteins. J Mol Biol 298:937–953

    Article  PubMed  CAS  Google Scholar 

  26. Noel JK, Whitford PC, Sanbonmatsu KY et al (2010) SMOG@ctbp: simplified deployment of structure-based models in GROMACS. Nucleic Acids Res 38:W657–W661

    Article  PubMed  CAS  Google Scholar 

  27. Sulkowska JI, Cieplak M (2008) Selection of optimal variants of Go-like models of proteins through studies of stretching. Biophys J 95:3174–3191

    Article  PubMed  CAS  Google Scholar 

  28. Noel JK, Whitford PC, Onuchic JN (2012) The shadow map: a general contact definition for capturing the dynamics of biomolecular folding and function. J Phys Chem B 116:8692–8702

    Article  PubMed  CAS  Google Scholar 

  29. Garcia LG, Pereira de Araujo AF (2006) Folding pathway dependence on energetic frustration and interaction heterogeneity for a three-dimensional hydrophobic protein model. Proteins 62:46–63

    Article  PubMed  CAS  Google Scholar 

  30. Capraro DT, Gosavi S, Roy M et al (2012) Folding circular permutants of IL-1beta: route selection driven by functional frustration. PLoS One 7:e38512

    Article  PubMed  CAS  Google Scholar 

  31. Hills RD Jr, Brooks CL III (2008) Subdomain competition, cooperativity, and topological frustration in the folding of CheY. J Mol Biol 382:485–495

    Article  PubMed  CAS  Google Scholar 

  32. Sobolev V, Sorokine A, Prilusky J et al (1999) Automated analysis of interatomic contacts in proteins. Bioinformatics 15:327–332

    Article  PubMed  CAS  Google Scholar 

  33. Clementi C (2008) Coarse-grained models of protein folding: toy models or predictive tools? Curr Opin Struct Biol 18:10–15

    Article  PubMed  CAS  Google Scholar 

  34. Hills RD Jr, Kathuria SV, Wallace LA et al (2010) Topological frustration in beta alpha-repeat proteins: sequence diversity modulates the conserved folding mechanisms of alpha/beta/alpha sandwich proteins. J Mol Biol 398:332–350

    Article  PubMed  CAS  Google Scholar 

  35. Karanicolas J, Brooks CL III (2003) Improved Go-like models demonstrate the robustness of protein folding mechanisms towards non-native interactions. J Mol Biol 334:309–325

    Article  PubMed  CAS  Google Scholar 

  36. Periole X, Allen LR, Tamiola K et al (2009) Probing the free energy landscape of the FBP28 WW domain using multiple techniques. J Comput Chem 30:1059–1068

    Article  PubMed  CAS  Google Scholar 

  37. Ivankov DN, Garbuzynskiy SO, Alm E et al (2003) Contact order revisited: influence of protein size on the folding rate. Protein Sci 12:2057–2062

    Article  PubMed  CAS  Google Scholar 

  38. Chan HS, Zhang Z, Wallin S et al (2011) Cooperativity, local-nonlocal coupling, and nonnative interactions: principles of protein folding from coarse-grained models. Annu Rev Phys Chem 62:301–326

    Article  PubMed  CAS  Google Scholar 

  39. Enciso M, Rey A (2011) Improvement of structure-based potentials for protein folding by native and nonnative hydrogen bonds. Biophys J 101:1474–1482

    Article  PubMed  CAS  Google Scholar 

  40. Kim J, Keyes T (2008) Influence of Go-like interactions on global shapes of energy landscapes in beta-barrel forming model proteins: inherent structure analysis and statistical temperature molecular dynamics simulation. J Phys Chem B 112:954–966

    Article  PubMed  CAS  Google Scholar 

  41. Zarrine-Afsart A, Wallin S, Neculai AM et al (2008) Theoretical and experimental demonstration of the importance of specific nonnative interactions in protein folding. Proc Natl Acad Sci USA 105:9999–10004

    Article  Google Scholar 

  42. Hills RD Jr, Brooks CL III (2008) Coevolution of function and the folding landscape: correlation with density of native contacts. Biophys J 95:L57–L59

    Article  PubMed  CAS  Google Scholar 

  43. Meireles L, Gur M, Bakan A et al (2011) Pre-existing soft modes of motion uniquely defined by native contact topology facilitate ligand binding to proteins. Protein Sci 20:1645–1658

    Article  PubMed  CAS  Google Scholar 

  44. Tama F, Brooks CL III (2006) Symmetry, form, and shape: guiding principles for robustness in macromolecular machines. Annu Rev Biophys Biomol Struct 35:115–133

    Article  PubMed  CAS  Google Scholar 

  45. Naganathan AN, Orozco M (2011) The protein folding transition-state ensemble from a Go-like model. Phys Chem Chem Phys 13:15166–15174

    Article  PubMed  CAS  Google Scholar 

  46. Cho SS, Pincus DL, Thirumalai D (2009) Assembly mechanisms of RNA pseudoknots are determined by the stabilities of constituent secondary structures. Proc Natl Acad Sci USA 106:17349–17354

    Article  PubMed  CAS  Google Scholar 

  47. Feng J, Walter NG, Brooks CL 3rd (2011) Cooperative and directional folding of the preQ1 riboswitch aptamer domain. J Am Chem Soc 133:4196–4199

    Article  PubMed  CAS  Google Scholar 

  48. Sosnick TR, Pan T (2004) Reduced contact order and RNA folding rates. J Mol Biol 342:1359–1365

    Article  PubMed  CAS  Google Scholar 

  49. Hills RD Jr, Lu L, Voth GA (2010) Multiscale coarse-graining of the protein energy landscape. PLoS Comput Biol 6:e1000827

    Article  PubMed  CAS  Google Scholar 

  50. Kamagata K, Kuwajima K (2006) Surprisingly high correlation between early and late stages in non-two-state protein folding. J Mol Biol 357:1647–1654

    Article  PubMed  CAS  Google Scholar 

  51. Naganathan AN, Munoz V (2005) Scaling of folding times with protein size. J Am Chem Soc 127:480–481

    Article  PubMed  CAS  Google Scholar 

  52. Zou T, Ozkan SB (2011) Local and non-local native topologies reveal the underlying folding landscape of proteins. Phys Biol 8:066011

    Article  PubMed  CAS  Google Scholar 

  53. Sieradzan AK, Scheraga HA, Liwo A (2012) Determination of effective potentials for the stretching of Ca-Ca virtual bonds in polypeptide chains for coarse-grained simulations of proteins from ab initio energy surfaces of N-methylacetamide and N-acetylpyrrolidine. J Chem Theory Comput 8:1334–1343

    Article  PubMed  CAS  Google Scholar 

  54. Peter C, Kremer K (2009) Multiscale simulation of soft matter systems: from the atomistic to the coarse-grained level and back. Phys Chem Chem Phys 5:4357–4366

    CAS  Google Scholar 

  55. Monticelli L, Kandasamy SK, Periole X et al (2008) The MARTINI coarse-grained force field: extension to proteins. J Chem Theory Comput 4:819–834

    Article  CAS  Google Scholar 

  56. Periole X, Cavalli M, Marrink SJ et al (2009) Combining an elastic network with a coarse-grained molecular force field: structure, dynamics, and intermolecular recognition. J Chem Theory Comput 5:2531–2543

    Article  CAS  Google Scholar 

  57. Winger M, Trzesniak D, Baron R et al (2009) On using a too large integration time step in molecular dynamics simulations of coarse-grained molecular models. Phys Chem Chem Phys 11:1934–1941

    PubMed  CAS  Google Scholar 

  58. Hess B, Kutzner C, van der Spoel D et al (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447

    Article  CAS  Google Scholar 

  59. Kwiecinska JI, Cieplak M (2005) Chirality and protein folding. J Phys Condens Matter 17:S1565–S1580

    Article  CAS  Google Scholar 

  60. Skrbic T, Micheletti C, Faccioli P (2012) The role of non-native interactions in the folding of knotted proteins. PLoS Comput Biol 8:e1002504

    Article  PubMed  CAS  Google Scholar 

  61. Okazaki K, Sato T, Takano M (2012) Temperature-enhanced association of proteins due to electrostatic interaction: a coarse-grained simulation of actin-myosin binding. J Am Chem Soc 134:8918–8925

    Article  PubMed  CAS  Google Scholar 

  62. Bellesia G, Jewett AI, Shea JE (2010) Sequence periodicity and secondary structure propensity in model proteins. Protein Sci 19:141–154

    Article  PubMed  CAS  Google Scholar 

  63. Chikenji G, Fujitsuka Y, Takada S (2006) Shaping up the protein folding funnel by local interaction: lesson from a structure prediction study. Proc Natl Acad Sci USA 103:3141–3146

    Article  PubMed  CAS  Google Scholar 

  64. Munoz V, Lopez EM, Jager M et al (1994) Kinetic characterization of the chemotactic protein from Escherichia coli, CheY. Kinetic analysis of the inverse hydrophobic effect. Biochemistry 33:5858–5866

    Article  PubMed  CAS  Google Scholar 

  65. Kathuria SV, Day IJ, Wallace LA et al (2008) Kinetic traps in the folding of beta alpha-repeat proteins: CheY initially misfolds before accessing the native conformation. J Mol Biol 382:467–484

    Article  PubMed  CAS  Google Scholar 

  66. Allen LR, Krivov SV, Paci E (2009) Analysis of the free-energy surface of proteins from reversible folding simulations. PLoS Comput Biol 5:e1000428

    Article  PubMed  CAS  Google Scholar 

  67. Mohazab AR, Plotkin SS (2009) Structural alignment using the generalized Euclidean distance between conformations. Int J Quantum Chem 109:3217–3228

    Article  CAS  Google Scholar 

  68. Accary JB, Teboul V (2012) Time versus temperature rescaling for coarse grain molecular dynamics simulations. J Chem Phys 136:094502

    Article  PubMed  CAS  Google Scholar 

  69. Lewis RJ, Muchova K, Brannigan JA et al (2000) Domain swapping in the sporulation response regulator SpoOA. J Mol Biol 297:757–770

    Article  PubMed  CAS  Google Scholar 

  70. Raman S, Lange OF, Rossi P et al (2010) NMR structure determination for larger proteins using backbone-only data. Science 327:1014–1018

    Article  PubMed  CAS  Google Scholar 

  71. Barducci A, Bonomi M, Derreumaux P (2011) Assessing the quality of the OPEP coarse-grained force field. J Chem Theory Comput 7:1928–1934

    Article  CAS  Google Scholar 

  72. Bereau T, Deserno M, Bachmann M (2011) Structural basis of folding cooperativity in model proteins: insights from a microcanonical perspective. Biophys J 100:2764–2772

    Article  PubMed  CAS  Google Scholar 

  73. Davtyan A, Schafer NP, Zheng W et al (2012) AWSEM-MD: protein structure prediction using coarse-grained physical potentials and bioinformatically based local structure biasing. J Phys Chem B 116:8494–8503

    Article  PubMed  CAS  Google Scholar 

  74. Enciso M, Rey A (2012) Simple model for the simulation of peptide folding and aggregation with different sequences. J Chem Phys 136:215103

    Article  PubMed  CAS  Google Scholar 

  75. Golas E, Maisuradze GG, Senet P et al (2012) Simulation of the opening and closing of Hsp70 chaperones by coarse-grained molecular dynamics. J Chem Theory Comput 8:1750–1764

    Article  PubMed  CAS  Google Scholar 

  76. Liwo A, Kazmierkiewicz R, Czaplewski C et al (1998) United-residue force field for off-lattice protein structure simulations. III. Origin of backbone hydrogen bonding cooperativity in united-residue potentials. J Comput Chem 19:259–276

    Article  CAS  Google Scholar 

  77. Sobolewski E, Oldziej S, Wisniewska M et al (2012) Toward temperature-dependent coarse-grained potentials of side-chain interactions for protein folding simulations. II. Molecular dynamics study of pairs of different types of interactions in water at various temperatures. J Phys Chem B 116:6844–6853

    Article  PubMed  CAS  Google Scholar 

  78. de Jong DH, Periole X, Marrink SJ (2012) Dimerization of amino acid side chains: lessons from the comparison of different force fields. J Chem Theory Comput 8:1003–1014

    Article  CAS  Google Scholar 

  79. Singh G, Tieleman DP (2011) Using the Wimley-White hydrophobicity scale as a direct quantitative test of force fields: the MARTINI coarse-grained model. J Chem Theory Comput 7:2316–2324

    Article  CAS  Google Scholar 

  80. Hall BA, Chetwynd AP, Sansom MS (2011) Exploring peptide-membrane interactions with coarse-grained MD simulations. Biophys J 100:1940–1948

    Article  PubMed  CAS  Google Scholar 

  81. Ward AB, Guvench O, Hills RD Jr (2012) Coarse grain lipid-protein molecular interactions and diffusion with MsbA flippase. Proteins 80:2178–2190

    Article  PubMed  CAS  Google Scholar 

  82. Seo M, Rauscher S, Pomes R et al (2012) Improving internal peptide dynamics in the coarse-grained MARTINI model: toward large-scale simulations of amyloid- and elastin-like peptides. J Chem Theory Comput 8:1774–1785

    Article  PubMed  CAS  Google Scholar 

  83. Bereau T, Deserno M (2009) Generic coarse-grained model for protein folding and aggregation. J Chem Phys 130:235106

    Article  PubMed  CAS  Google Scholar 

  84. Cheung MS, Finke JM, Callahan B et al (2003) Exploring the interplay between topology and secondary structural formation in the protein folding problem. J Phys Chem B 107:11193–11200

    Article  CAS  Google Scholar 

  85. Miyazawa S, Jernigan RL (1996) Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol 256:623–644

    Article  PubMed  CAS  Google Scholar 

  86. Kim YC, Hummer G (2008) Coarse-grained models for simulations of multiprotein complexes: application to ubiquitin binding. J Mol Biol 375:1416–1433

    Article  PubMed  CAS  Google Scholar 

  87. Givaty O, Levy Y (2009) Protein sliding along DNA: dynamics and structural characterization. J Mol Biol 385:1087–1097

    Article  PubMed  CAS  Google Scholar 

  88. Hyeon C, Thirumalai D (2005) Mechanical unfolding of RNA hairpins. Proc Natl Acad Sci USA 102:6789–6794

    Article  PubMed  CAS  Google Scholar 

  89. O'Brien EP, Christodoulou J, Vendruscolo M et al (2012) Trigger factor slows co-translational folding through kinetic trapping while sterically protecting the nascent chain from aberrant cytosolic interactions. J Am Chem Soc 134:10920–10932

    Article  PubMed  CAS  Google Scholar 

  90. Chu X, Wang Y, Gan L et al (2012) Importance of electrostatic interactions in the association of intrinsically disordered histone chaperone Chz1 and histone H2A.Z-H2B. PLoS Comput Biol 8:e1002608

    Article  PubMed  CAS  Google Scholar 

  91. Whitford PC, Noel JK, Gosavi S et al (2009) An all-atom structure-based potential for proteins: bridging minimal models with all-atom empirical forcefields. Proteins 75:430–441

    Article  PubMed  CAS  Google Scholar 

  92. Chu JW, Voth GA (2007) Coarse-grained free energy functions for studying protein conformational changes: a double-well network model. Biophys J 93:3860–3871

    Article  PubMed  CAS  Google Scholar 

  93. Maragakis P, Karplus M (2005) Large amplitude conformational change in proteins explored with a plastic network model: adenylate kinase. J Mol Biol 352:807–822

    Article  PubMed  CAS  Google Scholar 

  94. Noel JK, Schug A, Verma A et al (2012) Mirror images as naturally competing conformations in protein folding. J Phys Chem B 116:6880–6888

    Article  PubMed  CAS  Google Scholar 

  95. Whitford PC, Miyashita O, Levy Y et al (2007) Conformational transitions of adenylate kinase: switching by cracking. J Mol Biol 366:1661–1671

    Article  PubMed  CAS  Google Scholar 

  96. Singh JP, Whitford PC, Hayre NR et al (2012) Massive conformation change in the prion protein: using dual-basin structure-based models to find misfolding pathways. Proteins 80:1299–1307

    Article  PubMed  CAS  Google Scholar 

  97. Okazaki K, Takada S (2008) Dynamic energy landscape view of coupled binding and protein conformational change: induced-fit versus population-shift mechanisms. Proc Natl Acad Sci USA 105:11182–11187

    Article  PubMed  CAS  Google Scholar 

  98. Koga N, Takada S (2006) Folding-based molecular simulations reveal mechanisms of the rotary motor F-1-ATPase. Proc Natl Acad Sci USA 103:5367–5372

    Article  PubMed  CAS  Google Scholar 

  99. Ward A, Reyes CL, Yu J et al (2007) Flexibility in the ABC transporter MsbA: alternating access with a twist. Proc Natl Acad Sci USA 104:19005–19010

    Article  PubMed  CAS  Google Scholar 

  100. Tama F, Miyashita O, Brooks CL III (2004) Flexible multi-scale fitting of atomic structures into low-resolution electron density maps with elastic network normal mode analysis. J Mol Biol 337:985–999

    Article  PubMed  CAS  Google Scholar 

  101. Bacci M, Chinappi M, Casciola CM et al (2012) Role of denaturation in maltose binding protein translocation dynamics. J Phys Chem B 116:4255–4262

    Article  PubMed  CAS  Google Scholar 

  102. Chen J (2012) Towards the physical basis of how intrinsic disorder mediates protein function. Arch Biochem Biophys 524:123–131

    Article  PubMed  CAS  Google Scholar 

  103. Lee W, Zeng X, Rotolo K et al (2012) Mechanical anisotropy of ankyrin repeats. Biophys J 102:1118–1126

    Article  PubMed  CAS  Google Scholar 

  104. Shental-Bechor D, Arviv O, Hagai T et al (2010) Folding of conjugated proteins. Annu Rep Comput Chem 6:263–277

    Article  CAS  Google Scholar 

  105. Wang Q, Cheung MS (2012) A physics-based approach of coarse-graining the cytoplasm of Escherichia coli (CGCYTO). Biophys J 102:2353–2361

    Article  PubMed  CAS  Google Scholar 

  106. Whitford PC, Ahmed A, Yu Y et al (2011) Excited states of ribosome translocation revealed through integrative molecular modeling. Proc Natl Acad Sci USA 108:18943–18948

    Article  PubMed  CAS  Google Scholar 

  107. Bray JK, Weiss DR, Levitt M (2011) Optimized torsion-angle normal modes reproduce conformational changes more accurately than cartesian modes. Biophys J 101:2966–2969

    Article  PubMed  CAS  Google Scholar 

  108. Lu M, Ma J (2011) Normal mode analysis with molecular geometry restraints: bridging molecular mechanics and elastic models. Arch Biochem Biophys 508:64–71

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

R.D.H is grateful to the University of New England for startup funding, the Brooks Group for MD parameters, and Roy Johnston for providing coordinates.

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Sciences, University of New England, Portland, ME, USA

    Ronald D. Hills Jr.

Authors
  1. Ronald D. Hills Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Bioinformatics and Genomic, University of North Carolina at Charlott, Charlotte, North Carolina, USA

    Dennis R. Livesay

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media,New York

About this protocol

Cite this protocol

Hills, R.D. (2014). Balancing Bond, Nonbond, and Gō-Like Terms in Coarse Grain Simulations of Conformational Dynamics. In: Livesay, D. (eds) Protein Dynamics. Methods in Molecular Biology, vol 1084. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-658-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-62703-658-0_7

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-657-3

  • Online ISBN: 978-1-62703-658-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation