OPUS-PSP: An Orientation-dependent Statistical All-atom Potential Derived from Side-chain Packing

Journal of Molecular Biology

Volumn 376, Issue 1, 2008, Pages 288-301

  Lu, Mingyang ^a   Dousis, Athanasios D ^b   Ma, Jianpeng ^a,b  

^a BAYLOR COLLEGE OF MEDICINE   (United States)

^b Rice University   (United States)

Author keywords

decoy recognition structure prediction; knowledge based potential function; orientation dependence; protein folding; side chain packing

Indexed keywords

AMINO ACID; MEMBRANE PROTEIN;

ARTICLE; ATOM; CHEMICAL STRUCTURE; CONFORMATION; DATA BASE; PREDICTION; PRIORITY JOURNAL; PROTEIN INTERACTION; PROTEIN STRUCTURE; SCORING SYSTEM; STATISTICAL ANALYSIS;

EID: 38049051121     PISSN: 00222836     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmb.2007.11.033     Document Type: Article

References (45)

1. MacKerell A.D., Bashford Jr. D., Bellott M., Dunbrack Jr. R.L., Evanseck J.D., Field M.J., et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B102 (1998) 3586-3616
2. Skolnick J. In quest of an empirical potential for protein structure prediction. Curr. Opin. Struct. Biol. 16 (2006) 166-171
3. Bradley P., Malmstrom L., Qian B., Schonbrun J., Chivian D., Kim D.E., et al. Free modeling with Rosetta in CASP6. Proteins: Struct. Funct. Genet. 61 Suppl. 7 (2005) 128-134
4. Sippl M.J. Knowledge-based potentials for proteins. Curr. Opin. Struct. Biol. 5 (1995) 229-235
5. Jernigan R.L., and Bahar I. Structure-derived potentials and protein simulations. Curr. Opin. Struct. Biol. 6 (1996) 195-209
6. Moult J. Comparison of database potentials and molecular mechanics force fields. Curr. Opin. Struct. Biol. 7 (1997) 194-199
7. Lazaridis T., and Karplus M. Effective energy functions for protein structure prediction. Curr. Opin. Struct. Biol. 10 (2000) 139-145
8. Gohlke H., and Klebe G. Statistical potentials and scoring functions applied to protein-ligand binding. Curr. Opin. Struct. Biol. 11 (2001) 231-235
9. Russ W.P., and Ranganathan R. Knowledge-based potential functions in protein design. Curr. Opin. Struct. Biol. 12 (2002) 447-452
10. Buchete N.V., Straub J.E., and Thirumalai D. Development of novel statistical potentials for protein fold recognition. Curr. Opin. Struct. Biol. 14 (2004) 225-232
11. Poole A.M., and Ranganathan R. Knowledge-based potentials in protein design. Curr. Opin. Struct. Biol. 16 (2006) 508-513
12. Zhou Y., Zhou H., Zhang C., and Liu S. What is a desirable statistical energy function for proteins and how can it be obtained?. Cell Biochem. Biophys. 46 (2006) 165-174
13. Chen W.W., and Shakhnovich E.I. Lessons from the design of a novel atomic potential for protein folding. Protein Sci. 14 (2005) 1741-1752
14. Buchete N.V., Straub J.E., and Thirumalai D. Orientational potentials extracted from protein structures improve native fold recognition. Protein Sci. 13 (2004) 862-874
15. Mukherjee A., Bhimalapuram P., and Bagchi B. Orientation-dependent potential of mean force for protein folding. J. Chem. Phys. 123 (2005) 014901
16. Misura K.M., Morozov A.V., and Baker D. Analysis of anisotropic side-chain packing in proteins and application to high-resolution structure prediction. J. Mol. Biol. 342 (2004) 651-664
17. Miyazawa S., and Jernigan R.L. How effective for fold recognition is a potential of mean force that includes relative orientations between contacting residues in proteins?. J. Chem. Phys. 122 (2005) 024901
18. α positions. Protein Sci. 16 (2007) 1449-1463
19. Zhou H., and Zhou Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 11 (2002) 2714-2726
20. Shen M.Y., and Sali A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 15 (2006) 2507-2524
21. Lin M.S., Fawzi N.L., and Head-Gordon T. Hydrophobic potential of mean force as a solvation function for protein structure prediction. Structure 15 (2007) 727-740
22. Samudrala R., and Levitt M. Decoys 'R' Us: a database of incorrect conformations to improve protein structure prediction. Protein Sci. 9 (2000) 1399-1401
23. Rajgaria R., McAllister S.R., and Floudas C.A. A novel high resolution Calpha-Calpha distance dependent force field based on a high quality decoy set. Proteins: Struct. Funct. Genet. 65 (2006) 726-741
24. Tsai J., Bonneau R., Morozov A.V., Kuhlman B., Rohl C.A., and Baker D. An improved protein decoy set for testing energy functions for protein structure prediction. Proteins: Struct. Funct. Genet. 53 (2003) 76-87
25. Simons K.T., Kooperberg C., Huang E., and Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268 (1997) 209-225
26. John B., and Sali A. Comparative protein structure modeling by iterative alignment, model building and model assessment. Nucl. Acids Res. 31 (2003) 3982-3992
27. Gilis D. Protein decoy sets for evaluating energy functions. J. Biomol. Struct. Dynam. 21 (2004) 725-736
28. McConkey B.J., Sobolev V., and Edelman M. Discrimination of native protein structures using atom-atom contact scoring. Proc. Natl Acad. Sci. USA 100 (2003) 3215-3220
29. Simons K.T., Ruczinski I., Kooperberg C., Fox B.A., Bystroff C., and Baker D. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins: Struct. Funct. Genet. 34 (1999) 82-95
30. Zhang C., Liu S., Zhou H., and Zhou Y. An accurate, residue-level, pair potential of mean force for folding and binding based on the distance-scaled, ideal-gas reference state. Protein Sci. 13 (2004) 400-411
31. Zhang J., Chen R., and Liang J. Empirical potential function for simplified protein models: combining contact and local sequence-structure descriptors. Proteins: Struct. Funct. Genet. 63 (2006) 949-960
32. Lee M.C., Yang R., and Duan Y. Comparison between Generalized-Born and Poisson-Boltzmann methods in physics-based scoring functions for protein structure prediction. J. Mol. Model 12 (2005) 101-110
33. Wang G., and Dunbrack Jr. R.L. PISCES: a protein sequence culling server. Bioinformatics 19 (2003) 1589-1591
34. Skolnick J., Jaroszewski L., Kolinski A., and Godzik A. Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct?. Protein Sci. 6 (1997) 676-688
35. Miyazawa S., and Jernigan R.L. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 256 (1996) 623-644
36. Kuhlman B., Dantas G., Ireton G.C., Varani G., Stoddard B.L., and Baker D. Design of a novel globular protein fold with atomic-level accuracy. Science 302 (2003) 1364-1368
37. Park B., and Levitt M. Energy functions that discriminate X-ray and near native folds from well-constructed decoys. J. Mol. Biol. 258 (1996) 367-392
38. Samudrala R., Xia Y., Levitt M., and Huang E.S. A combined approach for ab initio construction of low resolution protein tertiary structures from sequence. Pac. Symp. Biocomput. (1999) 505-516
39. Xia Y., Huang E.S., Levitt M., and Samudrala R. Ab initio construction of protein tertiary structures using a hierarchical approach. J. Mol. Biol. 300 (2000) 171-185
40. Keasar C., and Levitt M. A novel approach to decoy set generation: designing a physical energy function having local minima with native structure characteristics. J. Mol. Biol. 329 (2003) 159-174
41. Dehouck Y., Gilis D., and Rooman M. A new generation of statistical potentials for proteins. Biophys. J. 90 (2006) 4010-4017
42. Dong Q., Wang X., and Lin L. Novel knowledge-based mean force potential at the profile level. BMC Bioinformat. 7 (2006) 324
43. Tobi D., and Elber R. Distance-dependent, pair potential for protein folding: results from linear optimization. Proteins: Struct. Funct. Genet. 41 (2000) 40-46
44. Colubri A., Jha A.K., Shen M.Y., Sali A., Berry R.S., Sosnick T.R., and Freed K.F. Minimalist representations and the importance of nearest neighbor effects in protein folding simulations. J. Mol. Biol. 363 (2006) 835-857
45. β statistical potentials can outperform all-atom potentials in decoy identification. Protein Sci. 16 (2007) 2123-2139