Ab initio protein folding

Current Opinion in Structural Biology

Volumn 10, Issue 2, 2000, Pages 146-152

  Osguthorpe, David J ^a  

^a BA2 7AY ^*  (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; PRIORITY JOURNAL; PROTEIN FOLDING; REVIEW; THERMODYNAMICS;

EID: 0034112809     PISSN: 0959440X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0959-440X(00)00067-1     Document Type: Review

References (63)

1. Anfinsen C. Principles that govern the folding of polypeptide chains. Science. 181:1973;223-230.
2. Moult J., Judson R., Fidelis K., Pedersen J.T. A large-scale experiment to assess protein structure prediction methods. Proteins. 23:1995;ii-iv.
3. Moult J., Hubbard T., Bryant S.H., Fidelis K., Pedersen J.T. Critical assessment of methods of protein structure prediction (CASP): round II. Proteins. (suppl 1):1997;2-6.
4. Moult J., Hubbard T., Fidelis K., Pedersen J.T. Critical assessment of methods of protein structure prediction (CASP): round III. Proteins. (suppl 3):1999;2-6. The organisers' view of the 1999 Critical assessment of methods of protein structure prediction experiment.
5. Srinivasan R., Rose G.D. LINUS: a hierarchic procedure to predict the fold of a protein. Proteins. 22:1995;81-99.
6. Avbelj F., Fele L. Prediction of the three-dimensional structure of proteins using the electrostatic screening model and hierarchic condensation. Proteins. 31:1998;74-96.
7. Pedersen J.T., Moult J. Protein folding simulations with genetic algorithms and a detailed molecular description. J Mol Biol. 269:1997;240-259.
8. Levitt M., Warshel A. Computer simulation of protein folding. Nature. 253:1975;694-698.
9. Simons K.T., Bonneau R., Ruczinski I., Baker D. Ab initio protein structure predictions of CASP III targets using ROSETTA. Proteins. (suppl 3):1999;171-176. A predictors' view of their Critical assessment of methods of protein structure prediction III folding predictions.
10. Lee J., Liwo A., Ripoll D.R., Pillardy J., Scheraga H.A. Calculation of protein conformation by global optimization of a potential energy function. Proteins. (suppl 3):1999;204-208. A predictors' view of their Critical assessment of methods of protein structure prediction III folding predictions.
11. Osguthorpe D.J. Improved ab initio predictions with a simplified, flexible geometry model. Proteins. (suppl 3):1999;186-193. A predictor's view of his Critical assessment of methods of protein structure prediction III folding predictions.
12. Liwo A., Oldziej S., Pincus M.R., Wawak R.J., Rackovsky S., Scheraga H.A. A united-residue force field for off-lattice protein-structure simulations. I. Functional forms and parameters of long-range side-chain interaction potentials from protein crystal data. J Comput Chem. 187:1997;849-873.
13. Samudrala R., Xia Y., Huang E., Levitt M. Ab initio protein structure prediction using a combined hierarchical approach. Proteins. (suppl 3):1999;194-198. A predictors' view of their Critical assessment of methods of protein structure prediction III folding predictions.
14. Reva B.A., Rykunov D.S., Finkelstein A.V., Skolnick J. Optimization of protein structure on lattices using a self-consistent approach. J Comput Biol. 53:1998;531-538.
15. Hao M., Scheraga H.A. Designing potential energy functions for protein folding. Curr Opin Struct Biol. 9:1999;184-188. A good overview of the state of potential development for protein folding, with emphasis on the optimisation of potential functions to discriminate the native from non-native structures.
16. Lazaridis T., Karplus M. Discrimination of the native from misfolded protein models with an energy function including implicit solvation. J Mol Biol. 288:1998;477-487. A physics-based potential using the CHARMM potential with an effective solvation potential derived from experimental solvation free energies was shown to discriminate native from misfolded structures as effectively as statistically derived potentials. Hence, there is no substance to the idea that statistical potentials are somehow superior to physical potentials, although statistical potentials are a lot easier to generate.
17. ••], is described and its derivation given. The significant change from earlier work is the much better reproduction of β sheets.
18. Liwo A., Oldziej S., Pincus M.R., Wawak R.J., Rackovsky S., Scheraga H.A. A united-residue force field for off-lattice protein-structure simulations. II. Parameterization of short-range interactions and determination of weights of energy terms by Z-score optimisation. J Comput Chem. 187:1997;874-887.
19. Liwo A., Kazmierkiewicz R., Czaplewski C., Groth M., Oldziej S., Wawak R.J., Rackovsky S., Pincus M.R., Scheraga H.A. United-residue force field for off-lattice protein-structure simulations: III. origin of backbone hydrogen-bonding cooperativity in united-residue potentials. J Comput Chem. 193:1998;259-276.
20. Robson B. Analysis of the code relating sequence to conformation in globular proteins. Biochem J. 141:1974;853-867.
21. Garnier J., Osguthorpe D.J., Robson B. Analysis of accuracy and implications of simple methods for predicting secondary structure of globular proteins. J Mol Biol. 120:1978;97-120.
22. Miyazawa S., Jernigan R.L. Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules. 18:1985;534-552.
23. Miyazawa S., Jernigan R.L. Evaluation of short-range interactions as secondary structure energies for protein fold and sequence recognition. Proteins. 36:1999;347-356.
24. Miyazawa S., Jernigan R.L. An empirical energy potential with a reference state for protein fold and sequence recognition. Proteins. 36:1999;357-369.
25. Zhu H., Braun W. Sequence specificity, statistical potentials, and three-dimensional structure prediction with self-correcting distance geometry calculations of β-sheet formation in proteins. Protein Sci. 8:1999;326-342.
26. Thomas P.D., Dill K.A. Statistical potentials extracted from protein structures: how accurate are they? J Mol Biol. 257:1996;457-469.
27. Miyazawa S., Jernigan R.L. Self-consistent estimation of inter-residue proteins contact energies based on an equilibrium mixture approximation of residues. Proteins. 34:1999;49-68.
28. Betancourt M.R., Thirumalai D. Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci. 8:1999;361-369.
29. Avbelj F., Fele L. Role of main-chain electrostatics, hydrophobic effect and side-chain conformational entropy in determining the secondary structure of proteins. J Mol Biol. 279:1998;665-684.
30. Avbelj F., Moult J. Role of electrostatic screening in determining protein main chain conformational preferences. Biochemistry. 34:1995;755-764.
31. Lee C., Levitt M. Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core. Nature. 352:1991;448-451.
32. Park B., Levitt M. Energy functions that discriminate X-ray and near-native folds from well-constructed decoys. J Mol Biol. 258:1996;367-392.
33. Godzik A., Kolinski A., Skolnick J. De-novo and inverse folding predictions of protein-structure and dynamics. J Comput Aided Mol Des. 7:1993;397-438.
34. Goldstein R.A., Luthey-Schulten Z.A., Wolynes P.G. Protein tertiary structure recognition using optimized Hamiltonians with local interactions. Proc Natl Acad Sci USA. 89:1992;9029-9033.
35. Harrison P.M., Chan H.S., Prusiner S.B., Cohen F.E. Thermodynamics of model prions and its implications for the problem of prion protein folding. J Mol Biol. 286:1999;593-606. Simple lattice models were used to model the thermodynamics of prion protein folding by folding polar/nonpolar sequences as monomers and homodimers. Sequences that have a dimer structure in which the monomer state is different from the monomer native state are seen to be associated with less stable monomer native structures. This provides a computable theoretical model for understanding aberrant folding diseases.
36. Cohen F.E. Protein misfolding and prion disease. J Mol Biol. 293:1999;313-320.
37. Hansmann U.H.E., Okamoto Y. New Monte Carlo algorithms for protein folding. Curr Opin Struct Biol. 9:1999;177-183. A review of Monte-Carlo-based search algorithms suitable for protein folding. In particular, this review focuses on enhanced sampling via generalised ensemble techniques, which can give significant improvements in search times for complicated potential functions.
38. Lee J., Liwo A., Scheraga H.A. Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K. Proc Natl Acad Sci USA. 96:1999;2025-2030.
39. F and the parameter σ as a measure of the foldability of sequences.
40. Konig R., Dandekar T. Improving genetic algorithms for protein folding simulations by systematic crossover. Biosystems. 50:1999;17-25.
41. Costas M.D., Floudas C.A. A deterministic global optimization approach for molecular structure determination. J Chem Phys. 100:1994;1247-1261.
42. Eyrich V.A., Standley D.M., Felts A.K., Friesner R.A. Protein tertiary structure prediction using a branch and bound algorithm. Proteins. 35:1999;41-57.
43. Huang E.S., Samudrala R., Ponder J.W. Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions. J Mol Biol. 290:1999;267-281.
44. Clamp M.E., Baker P.G., Stirling C.J., Brass A. Hybrid Monte Carlo: an efficient algorithm for condensed matter simulation. J Comput Chem. 15:1994;838-846.
45. Zhang H. A new hybrid Monte Carlo algorithm for protein potential function test and structure refinement. Proteins. 34:1999;464-471. A hybrid Monte Carlo algorithm, which consists of a short number of molecular dynamics iterations with a Metropolis Monte Carlo test after each set to determine whether to accept the new conformation or not, was modified to use a standard molecular mechanics potential during the molecular dynamics part and a knowledge-based (statistical) potential for the Metropolis test. In test systems, this method was shown to find lower energy conformations than other techniques for a few test systems and may be a useful tool for conformational searching.
46. Ishikawa K., Yue K., Dill K.A. Predicting the structures of 18 peptides using Geocore. Protein Sci. 8:1999;716-721.
47. Toma L., Toma S. Folding simulation of protein models on the structure-based cubo-octahedral lattice with the contact interactions algorithm. Protein Sci. 8:1999;196-202.
48. Pande V.S., Rokhsar D.S. Folding pathway of a lattice model for proteins. Proc Natl Acad Sci USA. 96:1999;1273-1278.
49. Sun Z., Xia X., Guo Q., Xu D. Protein structure prediction in a 210-type lattice model: parameter optimization in the genetic algorithm using orthogonal array. J Protein Chem. 181:1999;39-46.
50. Dinner A.R., Abkevich V., Shakhnovich E., Karplus M. Factors that affect the folding ability of proteins. Proteins. 35:1999;34-40. Simple lattice model protein folding studies were used to suggest that the Z-score is a good measure of the foldability of sequences.
51. 7 topomer sets for a typical 100-residue protein and that these sets could be searched in 0.1 s. The concept of topomers is interesting and is another way of classifying of structures as being close to native or not.
52. Chiu T., Goldstein R.A. Optimizing potentials for the inverse protein folding problem. Protein Eng. 119:1998;749-752.
53. Irback A., Peterson C., Potthast F., Sandelin E. Design of sequences with good folding properties in coarse-grained protein models. Structure. 73:1999;347-360.
54. de Beer T., Carter R.E., Lobel-Rice K.E., Sorkin A., Overdun M. Solution structure and Asn-Pro-Phe binding pocket of the Eps15 homology domain. Science. 281:1998;1357-1360.
55. Wei L.P., Huang E.S., Altman R.B. Are predicted structures good enough to preserve functional sites? Structure. 7:1999;643-650.
56. Koehl P., Levitt M. A brighter future for protein structure prediction. Nat Struct Biol. 62:1999;108-111.
57. Sternberg M.J.E., Bates P.A., Kelley L.A., MacCallum R.M. Progress in protein structure prediction: assessment of CASP3. Curr Opin Struct Biol. 9:1999;368-373.
58. Sippl M.J. Who solved the protein folding problem? Structure. 7:1999;R81-R83.
59. Shortle D. Structure prediction: the state of the art. Curr Biol. 96:1999;R205-R209.
60. Orengo C.A., Bray J.E., Hubbard T., LoConte L., Sillitoe I. Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction. Proteins. (suppl 3):1999;149-170. The sessors' view of the Critical assessment of methods of protein structure prediction III ab initio protein folding predictions.
61. Ortiz A.R., Kolinski A., Rotkiewicz P., Ilkowski B., Skolnick J. Ab initio folding of proteins using restraints derived from evolutionary information. Proteins. (suppl 3):1999;177-185. A predictors' view of their Critical assessment of methods of protein structure prediction III folding predictions.
62. Altschul S.F., Madden T.L., Schaffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:1997;3389-3402.
63. Samudrala R., Moult J. An all-atom distance dependent conditional probability discriminatory function for protein structure prediction. J Mol Biol. 275:1997;895-916.