Effective All-Atom Potentials for Proteins

  • Anders Irbäck
  • Sandipan Mohanty


Experimental developments are steadily improving our knowledge of the dynamics and interactions of proteins, knowledge that is important for understanding living systems at the molecular level. Computer simulations provide a complementary tool in this endeavor and offer unique opportunities to elucidate salient features that remain experimentally inaccessible. Simulations are being used to study many different aspects of protein dynamics of varying computational complexity, which makes it necessary to choose models for the calculations with care, depending on the problem at hand. The models in use today range from reduced models with one or a few sites per amino acid to all-atom models with explicit solvent molecules. All-atom simulations have traditionally often been based on quite elaborate potentials. This level of detail may be needed in many applications, like structure refinement, but is not an obvious choice when studying processes like folding and aggregation. Some recent (implicit solvent) models use simpler and computationally more convenient potentials, while retaining an all-atom description of the protein chains. This article discusses some potentials of this type. The usefulness of the approach is illustrated by briefly describing studies, based on one of the potentials, of folding thermodynamics, mechanical unfolding and aggregation.


Monte Carlo Conformation Space Helix Content Explicit Solvent Molecule Conventional Molecular Dynamic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Avbelj F, Moult J (1995) Role of electrostatic screening in determining protein main chain conformational preferences. Biochemistry 34:755–764PubMedCrossRefGoogle Scholar
  2. Blanco F, Rivas G, Serrano L (1994) A short linear peptide that folds into a native stable β-hairpin in aqueous solution. Nat Struct Biol 1:584–590PubMedCrossRefGoogle Scholar
  3. Cheon M, Chang I, Mohanty S, Luheshi LM, Dobson CM, Vendruscolo M, Favrin G (2007) Structural reorganisation and potential toxicity of oligomeric species formed during the assembly of amyloid fibrils. PLoS Comput Biol 3:e173CrossRefGoogle Scholar
  4. Chiti F, Dobson CM (2009) Amyloid formation by globular proteins under native conditions. Nat Chem Biol 5:15–22PubMedCrossRefGoogle Scholar
  5. Chung HS, Khalil M, Smith AW, Ganim Z, Tokmakoff A (2005) Conformational changes during the nanosecond-to-millisecond unfolding of ubiquitin. Proc Natl Acad Sci USA 102:612–617PubMedCrossRefGoogle Scholar
  6. Cordier F, Grzesiek S (2002) Temperature-dependence of protein hydrogen bond properties as studied by high-resolution NMR. J Mol Biol 317:739–752PubMedCrossRefGoogle Scholar
  7. Craig D, Gao M, Schulten K, Vogel V (2004) Tuning the mechanical stability of fibronectin type III modules through sequence variations. Structure 12:21–30PubMedCrossRefGoogle Scholar
  8. Dai QH, Thomas C, Fuentes EJ, Blomberg MRA, Dutton PL, Wand AJ (2002) Structure of a de novo designed protein model of radical enzymes. J Am Chem Soc 124:10952–10953PubMedCrossRefGoogle Scholar
  9. Ding F, Dokholyan NV, Buldyrev SV, Stanley HE, Shakhnovich EI (2002) Molecular dynamics simulation of the SH3 domain aggregation suggests a generic amyloidogenesis mechanism. J Mol Biol 324:851–857PubMedCrossRefGoogle Scholar
  10. Ding F, Tsao D, Nie H, Dokholyan NV (2008) Ab initio folding of proteins with all-atom discrete molecular dynamics. Structure 16:1010–1018PubMedCrossRefGoogle Scholar
  11. Dunker A, Brown C, Lawson J, Iakoucheva L (2002) Intrinsic disorder and protein function. Biochemistry 41:6573–6582PubMedCrossRefGoogle Scholar
  12. Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6:197–208PubMedCrossRefGoogle Scholar
  13. Favrin G, Irbäck A, Sjunnesson F (2001) Monte Carlo update for chain molecules: biased Gaussian steps in torsional space. J Chem Phys 114:8154–8158CrossRefGoogle Scholar
  14. Fesinmeyer RM, Hudson FM, Andersen NH (2004) Enhanced hairpin stability through loop design: the case of the protein G B1 domain hairpin. J Am Chem Soc 126:7238–7243PubMedCrossRefGoogle Scholar
  15. Forman JR, Clarke J (2007) Mechanical unfolding of proteins: insights into biology, structure and folding. Curr Opin Struct Biol 17:58–66PubMedCrossRefGoogle Scholar
  16. Frenkel D, Smit B (1996) Understanding molecular simulations: from algorithms to applications. Academic San Diego, CAGoogle Scholar
  17. Gnanakaran S, Nussinov R, Garcia, AE (2006) Atomic-level description of amyloid β-dimer formation. J Am Chem Soc 128:2158–2159PubMedCrossRefGoogle Scholar
  18. Gō N (1983) Theoretical studies of protein folding. Annu Rev Biophys Bioeng 12:183–210PubMedCrossRefGoogle Scholar
  19. Goux WJ, Kopplin L, Nguyen AD, Leak K, Rutkofsky M, Shanmuganandam VD, Sharma D, Inouye H, Kirschner DA (2004) The formation of straight and twisted filaments from short tau peptides. J Biol Chem 279:26868–26875PubMedCrossRefGoogle Scholar
  20. Herges T, Wenzel W (2004) An all-atom force field for tertiary structure prediction of helical proteins. Biophys J 87:3100–3109PubMedCrossRefGoogle Scholar
  21. Hubner IA, Deeds EJ, Shakhnovich EI (2005) High-resolution protein folding with a transferable potential. Proc Natl Acad Sci USA 102:18914–18919PubMedCrossRefGoogle Scholar
  22. Hwang W, Zhang S, Kamm RD, Karplus M (2004) Kinetic control of dimer structure formation in amyloid fibrillogenesis. Proc Natl Acad Sci USA 101:12916–12921PubMedCrossRefGoogle Scholar
  23. Imparato A, Pelizzola A (2008) Mechanical unfolding and refolding pathways of ubiquitin. Phys Rev Lett 100:158104PubMedCrossRefGoogle Scholar
  24. Irbäck A, Mitternacht S (2006) Thermal versus mechanical unfolding of ubiquitin. Proteins 65:759–766PubMedCrossRefGoogle Scholar
  25. Irbäck A, Mitternacht S (2008) Spontaneous β-barrel formation: an all-atom Monte Carlo study of Aβ16–22 oligomerization. Proteins 71:207–214PubMedCrossRefGoogle Scholar
  26. Irbäck A, Mitternacht S, Mohanty S (2005) Dissecting the mechanical unfolding of ubiquitin. Proc Natl Acad Sci USA 102:13427–13432PubMedCrossRefGoogle Scholar
  27. Irbäck A, Mitternacht S, Mohanty S (2009) An effective all-atom potential for proteins. PMC Biophys 2:2PubMedCrossRefGoogle Scholar
  28. Irbäck A, Mohanty S (2005) Folding thermodynamics of peptides. Biophys J 88:1560– 1569PubMedCrossRefGoogle Scholar
  29. Irbäck A, Mohanty S (2006) PROFASI: a Monte Carlo simulation package for protein folding and aggregation. J Comput Chem 27:1548–1555PubMedCrossRefGoogle Scholar
  30. Irbäck A, Samuelsson B, Sjunnesson F, Wallin S (2003) Thermodynamics of α- and β-structure formation in proteins. Biophys J 85:1466–1473PubMedCrossRefGoogle Scholar
  31. Johansson JS, Gibney BR, Skalicky JJ, Wand AJ, Dutton PL (1998) A nativelike three-α-helix bundle protein from structure-based redesign: a novel maquette scaffold. J Am Chem Soc 120:3881–3886CrossRefGoogle Scholar
  32. Kleiner A, Shakhnovich E (2007) The mechanical unfolding of ubiquitin through all-atom Monte Carlo simulation with a Gō-type potential. Biophys J 92:2054–2061PubMedCrossRefGoogle Scholar
  33. Klimov DK, Straub JE, Thirumalai D (2004) Aqueous urea solution destabilizes Aβ16–22 oligomers. Proc Natl Acad Sci USA 101:14760–14765PubMedCrossRefGoogle Scholar
  34. Klimov DK, Thirumalai D (2000) Native topology determines force-induced unfolding pathways in globular proteins. Proc Natl Acad Sci USA 97:7254–7259PubMedCrossRefGoogle Scholar
  35. Krone M, Hua L, Soto P, Zhou R, Berne B, Shea JE (2008) Role of water in mediating the assembly of Alzheimer amyloid-β Aβ16–22 protofilaments. J Am Chem Soc 130:11066–11072PubMedCrossRefGoogle Scholar
  36. Lazaridis T, Karplus M (1999) Effective energy function for proteins in solution. Proteins 35:133–152PubMedCrossRefGoogle Scholar
  37. Li D, Mohanty S, Irbäck A, Huo S (2008) Formation and growth of oligomers: a Monte Carlo study of an amyloid tau fragment. PLoS Comput Biol 4:e1000238PubMedCrossRefGoogle Scholar
  38. Li L, Huang HHL, Badilla CL, Fernandez JM (2005) Mechanical unfolding intermediates observed by single-molecule force spectroscopy in a fibronectin type III module. J Mol Biol 345:817–826PubMedCrossRefGoogle Scholar
  39. Li MS (2007) Secondary structure, mechanical stability, and location of transition state of proteins. Biophys J 93:2644–2654PubMedCrossRefGoogle Scholar
  40. Li MS, Kouza M, Hu CK (2007) Refolding upon force quench and pathways of mechanical and thermal unfolding of ubiquitin. Biophys J 92:547–561PubMedCrossRefGoogle Scholar
  41. Ma B, Nussinov R (2006) Simulations as analytical tools to understand protein aggregation and predict amyloid conformation. Curr Opin Chem Biol 10:445–452PubMedCrossRefGoogle Scholar
  42. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16:404–405PubMedCrossRefGoogle Scholar
  43. Meinke J, Hansmann UHE (2009) Free-energy driven folding and thermodynamics of the 67-residue protein GSαW –a large-scale Monte Carlo study. J Comput Chem 30:1642–1648Google Scholar
  44. Meinke JH, Hansmann UHE (2007) Aggregation of β-amyloid fragments. J Chem Phys 126:014706PubMedCrossRefGoogle Scholar
  45. Mitternacht S, Luccioli S, Torcini A, Imparato A, Irbäck A (2009) Changing the mechanical unfolding pathway of FnIII-10 by tuning the pulling strength. Biophys J 96:429–441PubMedCrossRefGoogle Scholar
  46. Munoz V, Thompson PA, Hofrichter J, Eaton WA (1997) Folding dynamics and mechanism of β-hairpin formation. Nature 390:196–199PubMedCrossRefGoogle Scholar
  47. Paci E, Karplus M (1999) Forced unfolding of fibronectin type 3 modules: an analysis by biased molecular dynamics simulations. J Mol Biol 288:441–459PubMedCrossRefGoogle Scholar
  48. Ponder JW, Case DA (2003) Force fields for protein simulations. Adv Protein Chem 66:27–85PubMedCrossRefGoogle Scholar
  49. Rapaport DC (1997) The art of molecular dynamics simulations. Cambridge University Press, CambridgeGoogle Scholar
  50. Röhrig UF, Laio A, Tantalo N, Parrinello M, Petronzio R (2006) Stability and structure of oligomers of the Alzheimer peptide Aβ16–22: from the dimer to the 32-mer. Biophys J 91:3217–3229PubMedCrossRefGoogle Scholar
  51. Santini S, Mousseau N, Derreumaux P (2004) In silico assembly of Alzheimer’s Aβ16–22 peptide into β-sheets. J Am Chem Soc 126:11509–11516PubMedCrossRefGoogle Scholar
  52. Sawaya MR, Sambashivan S, Nelson R, Ivanova MI, Sievers SA, Apostol MI, Thompson MJ, Balbirnie M, Wiltzius JJW, McFarlane HT, Madsen AØ, Riekel C, Eisenberg D (2007) Atomic structures of amyloid cross-β spines reveal varied steric zippers. Nature 447:453–457PubMedCrossRefGoogle Scholar
  53. Schlierf M, Li H, Fernandez JM (2004) The unfolding kinetics of ubiquitin captured with single-molecule force-clamp techniques. Proc Natl Acad Sci USA 101:7299–7304PubMedCrossRefGoogle Scholar
  54. Selkoe DJ (2003) Folding proteins in fatal ways. Nature 426:900–904PubMedCrossRefGoogle Scholar
  55. Shell MS, Ritterson R, Dill KA (2008) A test on peptide stability of amber force field with implicit solvation. J Phys Chem B 112:6878–6886PubMedCrossRefGoogle Scholar
  56. Smock RG, Gierasch LM (2009) Sending signals dynamically. Science 324:198–203PubMedCrossRefGoogle Scholar
  57. Sułkowska J, Cieplak M (2007) Mechanical stretching of proteins – a theoretical survey of the protein data bank. J Phys Condens Matter 19:283201CrossRefGoogle Scholar
  58. Uversky VN (2002) Natively unfolded proteins: a point where biology waits for physics. Protein Sci 11:739–756PubMedCrossRefGoogle Scholar
  59. Verma A, Schug A, Lee KH, Wenzel W (2006) Basin hopping simulations for all-atom protein folding. J Chem Phys 124:044515PubMedCrossRefGoogle Scholar
  60. Verma A, Wenzel W (2009) A free-energy approach for all-atom protein simulation. Biophys J 96:3483–3494PubMedCrossRefGoogle Scholar
  61. Yang JS, Chen WW, Skolnick J, Shakhnovich EI (2007) All-atom ab initio folding of a diverse set of proteins. Structure 15:53–63PubMedCrossRefGoogle Scholar
  62. Yang S, Cho SS, Levy Y, Cheung MS, Levine H, Wolynes PG, Onuchic JN (2004) Domain swapping is a consequence of minimal frustration. Proc Natl Acad Sci USA 101:13786–13791PubMedCrossRefGoogle Scholar
  63. Yoda T, Sugita Y, Okamoto Y (2004) Secondary-structure preferences of force fields for proteins evaluated by generalized-ensemble simulations. Chem Phys 307:269–283CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Computational Biology & Biological Physics, Department of Theoretical PhysicsLund UniversityLundSweden
  2. 2.Jülich Supercomputing CentreForschungszentrum Jülich GmbHJülichGermany

Personalised recommendations