Empirical potentials and functions for protein folding and binding

Current Opinion in Structural Biology

Volumn 7, Issue 2, 1997, Pages 222-228

  Vajda, Sandor ^a   Sippl, Manfred ^b   Novotny, Jiri ^c  

^a Boston University   (United States)

^b UNIVERSITY OF SALZBURG   (Austria)

^c BRISTOL MYERS SQUIBB PHARMACEUTICAL RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

POLYPEPTIDE;

ELECTRIC POTENTIAL; ENERGY; GEOMETRY; HYDROGEN BOND; HYDROPHOBICITY; LIGAND BINDING; MODEL; PRIORITY JOURNAL; PROTEIN BINDING; PROTEIN FOLDING; REVIEW; SOLVATION;

EID: 0030968991     PISSN: 0959440X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0959-440X(97)80029-2     Document Type: Article

References (71)

1. Novotny J, Rashin AA, Bruccoleri RE: Criteria that discriminate between native proteins and incorrectly folded models. Proteins 1988, 4:19-30.
2. Shoichet BK, Kuntz ID: Protein docking and complementarity. J Mol Biol 1991, 221:327-346.
3. Brady GP, Sharp KA: Decomposition of interaction free energies in proteins and other complex systems. J Mol Biol 1995, 17:77-85.
4. Novotny J, Bruccoleri RE, Saul FA: On the attribution of binding energy in the antigen-antibody complexes McPC 603, D1.3 and HyHEL-5. Biochemistry 1989 28:4735-4749.
5. Vajda S, Weng Z, Rosenfeld R, DeLisi C: Effect of conformational flexibility and solvation on receptor-ligand binding free energies. Biochemistry 1994, 33:13977-13988.
6. Honig B, Nicholls A: Classical electrostatics in biology and chemistry. Science 1995, 268:1144-1149.
7. Dill KA, Bromberg S, Yue K, Fiebig KM, Yee DP, Thoma PD, Chan HS: Principles of protein folding - a perspective from simple exact models. Protein Sci 1995, 4:561-602.
8. Sali A, Shaknovich E, Karplus M: Kinetics of protein folding: a lattice model study of the requirements for folding to the native state. J Mol Biol 1994, 235:1614-1636.
9. Miyazava S, Jernigan R: Residue-residue potentials with a favorable contact pair term and unfavorable high packing density term, for simulation and threading. J Mol Biol 1996, 256:623-644. This paper shows that the re-evaluation of the classical Miyazava-Jernigan potential, using a significantly larger set of protein crystal structures, leaves the interaction parameters almost unchanged. The authors make an important observation that the estimates of hydrophobicity from the contact energies for nonpolar sidechains agree well with the experimental values, in other words, the potential can be used to calculate hydrophobic contributions to the free energy. Whereas the original potential included only attractive terms, repulsive packing energy terms have now been added that are operative at high densities.
10. Chan HS, Dill KA: Comparing folding codes for proteins and polymers. Proteins 1996, 24:335-344. Simple potentials (folding codes) are studied on 2D lattice models. The properties of interests are degeneracy (the number of sequences that fold to a given number of structures) and encodability (the number of folds that are unique lowest energy structures of certain monomer sequences). This paper also emphasizes the role of the correlations in the folding code. Correlation in a lattice model means that all instances of an interaction between residue types i and j must have the same energy.
11. Unger R, Moult J: Local interactions dominate folding in a simple protein model. J Mol Biol 1996, 259:988-994. This paper shows that the full characterization of simple potentials, including thermodynamic and kinetic consequences, is far from simple even on 3D lattice models. In particular, the question of foldability on a 3D lattice with a random pairwise-interaction potential is re-examined. One of the main results of Sali et al. [8] is that the sufficient and necessary condition for folding in this model is the existence of a pronounced energy minimum. Further simulations by Unger and Moult in this paper reveal that this conclusion is true if, and only if, a specific folding temperature is selected for each individual protein. The second problem studied is the effect of local (i.e. between residues close in the sequence) versus nonlocal interactions. Sequences with many possible strong local interactions are shown to be easy to fold. This result also contradicts some conclusions that have appeared in the literature [12].
12. Abkevics VI, Gutin AM, Shaknovich EI: Impact of local and non-local interactions on thermodynamics and kinetics of protein folding. J Mol Biol 1995, 252:460-471.
13. Godzik A, Kolinski A, Skolnick J: Lattice representations of globular proteins: how good are they? J Comp Chem 1993, 10:1194-1202.
14. Reva BA, Sanner MF, Olson AJ, Finkelstein AV: Lattice modeling: accuracy of energy calculations. J Comp Chem 1996, 17:1025-1032. This paper emphasizes that energy calculations based on lattice models are always approximate, because the distances between chain links, and hence the interaction energies, are distorted. The distortions can be reduced by using a fine enough lattice, or by adjusting the lattice spacing.
15. Honig B, Cohen FE: Adding backbone to protein folding: why proteins are polypeptides? Fold Des 1996, 1:R17-R20. This review recalls the importance of the hydrogen-bonding structure, which may have been lost because the energetic contribution of hydrogen bonds is relatively minor. Burying polar groups, however, is strongly penalized unless they can form hydrogen bonds. Sidechain-only models fail to account for the penalty associated with the burial of backbone atoms, and hence have difficulty capturing essential features of protein folding.
16. Levitt M, Warshel A: Computer simulations of protein folding. Nature 1975, 253:694-698.
17. Hagler AT, Honig B: On the formation of protein tertiary structure. Proc Natl Acad Sci USA 1978, 2:554-558.
18. Kuntz ID, Crippen GM, Kollman PA, Kimelman D: Calculation of protein tertiary structure. J Mol Biol 1976, 106:983-994.
19. Bowie JU, Eisenberg D: An evolutionary approach to folding small alpha-helical proteins that uses sequence information and an empirical guiding fitness function. Proc Natl Acad Sci USA 1994, 91:4436-4440.
20. Monge A, Friesner RA, Honig B: An algorithm to generate low-resolution tertiary structures from knowledge of secondary structure. Proc Natl Acad Sci USA 1994, 91:5027-5029.
21. Brasseur R: Simulating the folding of small proteins by use of the local minimum energy and the free solvation energy yields native-like structures. J Mol Graph 1995, 13:312-322. Simulations are based on a potential that includes a complete molecular mechanics energy function and two solvation terms: the first representing the effect of solvation on the interactions between solute atoms; the second representing the effect of solvation on the interactions between solute and solvent. These terms are very probably not independent, and thus the model includes more parameters than necessary. Its application to small proteins yields good results.
22. Wilson C, Doniach SA: A computer model to dynamically simulate protein folding: studies with crambin. Proteins 1989, 6:193-209.
23. Koretke KK, Luthey-Schulten Z, Wolynes PG: Self-consistently optimized statistical mechanical energy functions for sequence structure alignment Protein Sci 1996, 5:1043-1059.
24. Dandekar T, Argos P: Ab initio tertiary-fold prediction of helical and non-helical protein chains using a genetic algorithm. Int J Biol Macromol 1996 18:14.
25. Kolinski A, Skolnick J: Monte-Carlo simulations of protein folding. 1. Lattice model and interaction scheme. Proteins 1994, 18:338-352.
26. Kolinski A, Skolnick J: Monte-Carlo simulations of protein folding. 2. Application to protein-A, ROP, and crambin. Proteins 1994, 18:353-366.
27. Srinivasan R, Rose GD: LINUS: a hierarchical procedure to predict the fold of a protein. Protein Sci 1995, 22:81-99. This paper received tremendous publicity due to the reported progress in predicting the folds of protein fragments of up to 50 residues. A simplified geometry and a very simple potential have been used. The search is based on a MC-type method in which a single move alters the torsional angles of three consecutive residues. Whenever a chain segment adopts a conformation that is persistent in a number of moves, it is constrained to remain in the same conformation throughout the simulation.
28. Sun S, Thomas PD, Dill KA: A simple protein folding algorithm using binary code and secondary structure constraints. Protein Eng 1995, 8:769-778.
29. Yue K, Dill KA: Folding proteins with a simple energy function and extensive conformational searching. Protein Sci 1996, 5:254-261. This paper extends the method of folding small proteins described in [28]. The potential includes only two terms, representing favorable interactions due to hydrophobic contacts and unfavorable interactions due to unsatisfied polar burials. Native-like structures of four small proteins are determined as a compromise between these two effects. The advantages of using folding models with very few parameters are discussed.
30. ••], the balance between hydrophobic and hydrogen-bond contributions is emphasized. Application to the folding of crambin yields, as one of the lowest energy structures, a conformation that has 3 Å rmsd from the crystal structure. The disulfide-bond information is not exploited in this example.
31. Sung SS: Folding simulations of alanine-based peptides with lysin residues. Biophys J 1995, 68:826-834.
32. Chiche L, Gregoret LM, Cohen FE, Kollman PA: Protein model structure evaluation using the solvation free energy of folding. Proc Natl Acad Sci USA 1990, 87:3240-3243.
33. Cardozo T, Totrov M, Abagyan R: Homology modeling by the ICM method. Proteins 1995, 23:403-414.
34. Sun S: A genetic algorithm that seeks native states of peptides and proteins. Biophys J 1995, 69:340-355. This simulation is based on simple polypeptide geometry and a potential that includes structure-based nonlocal terms, and a local term among nearest neighbor amino acids. The nonlocal terms are contact potentials representing backbone-to-backbone and sidechain-to-sidechain interactions. The local torsional term is extracted from the results of detailed molecular mechanics calculations. Applications include predicting the structure of crambin with 3 Å rmsd from the crystal structure.
35. Fraternali F, Van Gunsteren WF: An efficient mean solvation force model for use in molecular dynamics simulations of proteins in aqueous solution. J Mol Biol 1996, 256:939-948. Two atomic solvation terms are added to the GROMOS potential, one to reward exposed polar area, the other to penalize exposed nonpolar area. The extension of the potential significantly improves the agreement with the properties obtained in molecular dynamics simulations with explicit water. In particular, the main artefacts typically encountered in in vacuo simulations are considerably reduced.
36. Second meeting on the critical assessment of techniques for protein prediction on World Wide Web URL: http://iris4.carb.nist.gov/casp2
37. Gilis D, Rooman M: Stability changes upon mutation of solvent-accessible residues in proteins evaluated by database-derived potentials. J Mol Biol 1996, 257:1112-1126. This paper includes a general discussion of the formalism for deriving effective potentials from known structures.
38. Sippl MJ, Ortner M, Jaritz M, Lackner P, Floeckner HL: Helmholtz free energy of atom pair interactions in proteins. Fold Des 1996, 1:289-298.
39. Sippl MJ: Calculation of conformational ensembles from potentials of the main force. J Mol Biol 1990, 213:167-180.
40. Sippl MJ: Helmholtz free energy of peptide hydrogen bonds in proteins. J Mol Biol 1996, 220:644-648.
41. Jernigan RL, Bahar I: Structure-derived potentials and protein simulations. Curr Opin Struct Biol 1996, 6:195-209.
42. ••].
43. Thomas PD, Dill KA: An iterative method for extracting energy-like quantities from protein structures. Proc Natl Acad Sci USA 1996, 93:11628-11633.
44. Zhang C, Vasmatzis G, Cornette JL, DeLisi C: Determination of atomic desolvation energies from the structures of crystallised proteins. J Mol Biol 1997, in press. The authors extend the Miyazava-Jernigan potential to the atomic level, and determine contact energies for 18 different atom types. The free energies of solvating amino acid sidechains have been calculated using this method and are found to correlate to a very high degree (r=0.975) with experimentally determined free energies of transferring the sidechains between octanol and water. Exploiting this property, the atomic contact energy has been used for the calculation of the solvation contribution to the binding free energy.
45. DeBolt SE, Skolnick J: Evaluation of atomic level mean force potentials via inverse refinement of protein structures: atomic burial position and pairwise non-bonded interactions. Protein Eng 1996, 8:637-655. Structure-based potentials are evaluated in terms of the problem of distinguishing native and slightly misfolded conformations. The ability of combining knowledge-based and molecular mechanics terms are studied, and the use of such combined potentials in minimization is discussed.
46. Gschwend DA, Good AC, Kuntz ID: Molecular docking toward drug design. J Mol Recog 1996, 9:175-186. A well written review of both rigid and flexible docking procedures, with some emphasis on the successful DOCK program. The problem of ranking docked complexes and the development of empirical target functions are discussed.
47. Norel R, Lin SL, Wolfson HJ, Nussinov R: Molecular surface complementarity at protein-protein interfaces - the critical role played by surface normals at well placed, sparse points in docking. J Mol Biol 1995, 252:263-273. This paper describes the use of normals to the surface at specific points as shape descriptor for efficient rigid-body docking.
48. Vakser I: Low-resolution docking: prediction of complexes for undetermined structures. Biopolymers 1996, 39:455-464. This paper extends the correlation technique introduced by the author. Increasing the range of interactions in a simple potential, defined in terms of attractive and repulsive step functions, is shown to remove the local minima in an exhaustive search.
49. Weng Z, Vajda S, DeLisi C: Prediction of complexes using empirical free energy functions. Protein Sci 1996, 5:614-626. A partition-type binding free-energy evaluation model [5], which has an atomic parameter model of the solvation, is shown to discriminate native and nonnative docked conformations and, to yield binding free energies that are within 5% of the experimentally determined values. General aspects of the effective evaluation of binding free energy and the problems associated with the use of continuum electrostatics methods are discussed.
50. Ajay, Murcko MA: Computational methods to predict binding free energy in ligand-receptor complexes. J Med Chem 1995, 38:4953-4967. A thoughtful review of current approaches to the evaluation of binding free energy including a classification of various methods is presented.
51. Horton N, Lewis, M Calculation of the free energy of association for protein complexes. Protein Sci 1992 1:169-181.
52. Nauchitel V, Villaverde MC, Sussman F: Solvent accessibility as a predictive tool for the free energy of inhibitor binding to the HIV-1 protease. Protein Sci 1995, 4:1356-1364.
53. Viswanadhan VN, Reddy MR, Wlodawer A, Varney MD, Weinstein JN: An approach to rapid estimation of relative binding affinities of enzyme inhibitors: application to peptidomimetic inhibitors of the human immunodeficiency virus type 1 protease. J Med Chem 1996, 39:705-712.
54. •], the potential is not dominated by the overall hydrophobic attraction.
55. •].
56. DeWitte RS, Shaknovich EI: SMoG: De novo design method based on simple, fast, an accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 1996, 118:11733-11744.
57. Bohm HJ: The development of a simple empirical scoring function to estimate the binding constant for a protein ligand complex of known three-dimensional structure. J Comp Aid Mol Des 1994, 8:243-256.
58. Laskowski RA, Thornton JM, Humblet C, Singh J: X-SITE-use of empirically derived atomic packing preferences to identify favourable interaction regions in the binding sites of proteins. J Mol Biol 1996, 259:175-201.
59. Verkhivker G, Appelt K, Freer ST, Villafranca JE: Empirical free energy calculations of ligand-protern crystallographic complexes. 1. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity. Protein Eng 1995, 8:677-691.
60. Holloway MK, Wai JM, Halgren TA: A priori predictions of activity for HIV-1 protease inhibitors employing energy minimization in the active site. J Med Chem 1995, 38:305-317.
61. Head RD Smyte ML Oprea TI, Waller CL, Green SM, Marshall GR: VALIDATE: a new method for receptor-based prediction of binding affinities of novel ligands. J Am Chem Soc 1996, 118:3959-3969.
62. Zhang T, Koshland DE Jr: Computational method for relative binding energies of enzyme substrate complexes. Protein Sci 1996, 5:348-356. The continuum electrostatic approach has been applied to 63 pairs of nine mutant proteins with seven different substituted substrates. Only relative binding free energies are given for each complex using the complex of the natural protein and the unsubstituted substrate as the reference state. Thus, the results, which are in good agreement with the relative free energies observed, show the effects of substitutions in the substrate, but not the effects of mutations in the protein.
63. •]. The continuum model distinguishes near-native from nonnative conformations very well but ranks the binding free energies of the three different complexes incorrectly.
64. Cummings MD, Hart TN, Read, RJ: Atomic solvation parameters in the analysis of protein-protein docking results. Protein Sci 1995, 4:2087-2099.
65. Juffer AH, Eisenhaber F, Hubbard SJ, Walther D, Argos P: Comparison of atomic solvation parametric sets: applicability and limitations in protein folding and binding. Protein Sci 1995, 4:2499-2509. A detailed account of the various atomic solvation parameter values that have been published is given.
66. -2 value based on octanol-to-water transfer.
67. Bruccoleri RE, Novotny J, Davis ME, Sharp KA: Finite difference Poisson-Boltzmann electrostatic calculations: increased accuracy achieved by harmonic dielectric smoothing and charge antialiasing. J Comp Chem 1997, 18:268-276.
68. Novotny J, Bruccoleri RE, Davis ME, Sharp KA: Empirical free energy calculations: a blind test and further improvements to the method. J Mol Biol 1997, in press.
69. Smith TF: Models of protein folding. Science 1995, 268:958-959.
70. Bruccoleri RE, Karplus M: Prediction of folding of short polypeptide segments by uniform conformational sampling. Biopolymers 1987, 26:137-168.
71. Bruccoleri RE: Application of systematic conformational search to protein modeling. Mol Simulat 1993, 10:151-174.