Advertisement

Scoring Functions for De Novo Protein Structure Prediction Revisited

  • Shing-Chung Ngan
  • Ling-Hong Hung
  • Tianyun Liu
  • Ram Samudrala
Part of the Methods in Molecular Biology™ book series (MIMB, volume 413)

Summary

De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, without the use of explicit templates. A general paradigm for de novo prediction involves sampling the conformational space, guided by scoring functions and other sequence-dependent biases, such that a large set of candidate (“decoy”) structures are generated, and then selecting native-like conformations from those decoys using scoring functions as well as conformer clustering. High-resolution refinement is sometimes used as a final step to fine-tune native-like structures. There are two major classes of scoring functions. Physics-based functions are based on mathematical models describing aspects of the known physics of molecular interaction. Knowledge-based functions are formed with statistical models capturing aspects of the properties of native protein conformations. We discuss the implementation and use of some of the scoring functions from these two classes for de novo structure prediction in this chapter.

Keywords

De novo physics-based knowledge-based potential protein folding 

Notes

Acknowledgments

We thank Drs. Enoch Huang and Britt Park for their earlier edition on scoring functions for de novo protein structure prediction and the anonymous reviewer for the many helpful suggestions. This work is supported in part by a Searle Scholar Award, NSF Grant DBI-0217241, an NSF CAREER award, and NIH Grant GM068152 to R.S. and the University of Washington’s Advanced Technology Initiative in Infectious Diseases.

References

  1. 1.
    Brenner, S., Levitt, M. (2000) Expectations from structural genomics. Protein Sci., 9, 197–200.PubMedCrossRefGoogle Scholar
  2. 2.
    Brenner, S.E. (2001) A tour of structural genomics. Nat. Genet., 210, 801–809.Google Scholar
  3. 3.
    Burley, S.K. (2000) An overview of structural genomics. Nat. Struct. Biol., 7 (Suppl), 932–934.PubMedCrossRefGoogle Scholar
  4. 4.
    Heinemann, U., Illing, G., Oschkinat, H. (2001) High-throughput three-dimensional protein structure determination. Curr. Opin. Biotech., 12, 348–354.PubMedCrossRefGoogle Scholar
  5. 5.
    Bonneau, R., Baker, D. (2001) Ab initio protein structure prediction: progress and prospects. Annu. Rev. Biophys. Biomol. Struct., 30, 173–189.PubMedCrossRefGoogle Scholar
  6. 6.
    Anfinsen, C.B., Haber, E., Sela, M., White, F.H., Jr. (1961) The kinetics of formation of active ribonuclease during oxidation of the reduced polypeptide chain. Proc. Natl. Acad. Sci. U. S. A., 47, 1309–1314.PubMedCrossRefGoogle Scholar
  7. 7.
    Doolittle, R. (1981) Similar amino acid sequences: chance or common ancestry? Science, 214, 149–159.PubMedCrossRefGoogle Scholar
  8. 8.
    Sander, C., Schneider, R. (1991) Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins, 9, 56–68.PubMedCrossRefGoogle Scholar
  9. 9.
    Murzin, A., Bateman, A. (1997) Distance homology recognition using structural classification of proteins. Proteins, 29S, 105–112.CrossRefGoogle Scholar
  10. 10.
    Bowie, J., Luthy, R., Eisenberg, D. (1991) Method to identify protein sequences that fold into a known three-dimensional structure. Science, 253, 164–170.PubMedCrossRefGoogle Scholar
  11. 11.
    Jones, D., Taylor, W., Thornton, J. (1992) A new approach to protein fold recognition. Nature, 258, 86–89.CrossRefGoogle Scholar
  12. 12.
    Moult, J., Fidelis, K., Zemla, A. Hubbard, T. (2003) Critical assessment of methods of protein structure prediction (CASP): round V. Proteins, 53, 334–339.PubMedCrossRefGoogle Scholar
  13. 13.
    Moult, J., Fidelis, K., Rost, B., Hubbard, T., Tramontano, A. (2005) Critical assessment of methods of protein structure prediction (CASP) – round 6. Proteins, 61, 3–7.PubMedCrossRefGoogle Scholar
  14. 14.
    Lee, J., Liwo, A., Ripoll, D., Pillardy, J., Scheraga, J. (1999) Calculation of protein conformation by global optimization of a potential energy function. Proteins, S3, 204–208.CrossRefGoogle Scholar
  15. 15.
    Samudrala, R., Xia, Y., Huang, E., Levitt, M. (1999) Ab initio protein structure prediction using a combined hierarchical approach. Proteins, S3, 194–198.CrossRefGoogle Scholar
  16. 16.
    Simons, K., Bonneau, R., Ruczinski, I., Baker, D. (1999) Ab initio structure prediction of CASP3 targets using ROSETTA. Proteins, S3, 171–176.CrossRefGoogle Scholar
  17. 17.
    Samudrala, R., Xia, Y., Levitt, M., Huang E.S. (1999) A combined approach for ab initio construction of low resolution protein tertiary structures from sequence, in Proceedings of the Pacific Symposium on Biocomputing (Altman, R. B., Dunker, A.K., Hunter, L., Klein, T.E., Lauderdale, K., eds.), World Scientific Press, Singapore, pp. 505–516.Google Scholar
  18. 18.
    Samudrala, R., Levitt, M. (2002) A comprehensive analysis of 40 blind protein structure predictions. BMC Struct Biol, 2, 3–18.PubMedCrossRefGoogle Scholar
  19. 19.
    Moult, J., Hubbard, T., Bryant, S.H., Fidelis, K., Pedersen, J.T. (1997) Critical assessment of methods of protein structure prediction (CASP): round II. Proteins, 29, 2–6.CrossRefGoogle Scholar
  20. 20.
    Moult, J., Hubbard, T., Fidelis, K., Pedersen, J.T. (1999) Critical assessment of methods of protein structure prediction (CASP): round III. Proteins, 37, 2–6.CrossRefGoogle Scholar
  21. 21.
    Moult, J., Fidelis, K., Zemla, A., Hubbard, T. (2001) Critical assessment of methods of protein structure prediction (CASP): round IV. Proteins, 45, 2–7.CrossRefGoogle Scholar
  22. 22.
    Brooks, B., Bruccoleri, R., Olafson, B., States, D., Swaminathan, S., Karplus, M. (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comp. Chem., 4, 187–217.CrossRefGoogle Scholar
  23. 23.
    Weiner, S., Kollman P., Nguyen, D., Case, D. (1986) An all atom force field for simulations of proteins and nucleic acids. J. Comp. Chem., 7, 230–252.CrossRefGoogle Scholar
  24. 24.
    Jorgensen, W., Tirado-Rives, J. (1988) The OPLS potential function for proteins. Energy minimisations for crystals of cyclic peptides and crambin. J. Amer. Chem. Soc., 110, 1657–1666.CrossRefGoogle Scholar
  25. 25.
    MacKerell, A.D., Jr., Bashford, D., Bellott, M., Dunbrack, R.L., Jr., Evanseck, J.D., et al. (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B, 102, 3586–3616.CrossRefGoogle Scholar
  26. 26.
    Cornell, W.D., Cieplak, P., Bayly, C.I., Gould, I.R., Merz, K.M., Jr., Fergusson, D.M., Spellmeyer, D.C., Fox, D.C., Caldwell, J.W., Kollman, P.A. (1995) A second generation force field for the simulation of proteins and nucleic acids. J. Amer. Chem. Soc., 117, 5179–5197.CrossRefGoogle Scholar
  27. 27.
    Nemethy, G., Gibson, K.D., Palmer, K.A., Yoon, C.N., Paterlini, G., Zagari, A., Rumsey, S., Scheraga, H.A. (1992) Energy parameters in peptides: improved geometrical parameters and non-bonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. J. Phys. Chem., 96, 6472–6484.CrossRefGoogle Scholar
  28. 28.
    Wodak, S., Rooman, M. (1993) Generating and testing protein folds. Curr. Opin. Struct. Biol., 3, 247–259.CrossRefGoogle Scholar
  29. 29.
    Sippl, M. (1995) Knowledge based potentials for proteins. Curr. Opin. Struct. Biol., 5, 229–235.PubMedCrossRefGoogle Scholar
  30. 30.
    Gilis, D., Rooman, M. (1996) Stability changes upon mutation of solvent-accessible residues in proteins evaluated by database-derived potentials. J. Mol. Biol., 257, 1112–1126.PubMedCrossRefGoogle Scholar
  31. 31.
    Jernigan, R.L., Bahar I. (1996) Structure-derived potentials and protein simulations. Curr. Opin. Struct. Biol., 6, 195–209.PubMedCrossRefGoogle Scholar
  32. 32.
    DeBolt, S.E., Skolnick, J. (1996) Evaluation of atomic level mean force potentials via inverse refinement of protein structures: atomic burial position and pairwise non-bonded interactions. Protein Eng., 8, 637–655.CrossRefGoogle Scholar
  33. 33.
    Zhang, C., Vasmatzis, G., Cornette, J.L., DeLisi, C. (1997) Determination of atomic desolvation energies from the structures of crystallised proteins. J. Mol. Biol., 267, 707–726.PubMedCrossRefGoogle Scholar
  34. 34.
    Samudrala, R., Moult, J. (1998) An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. J. Mol. Biol., 275, 895–916.PubMedCrossRefGoogle Scholar
  35. 35.
    Huang, E.S., Samudrala, R., Park, B.H. (2000) Scoring functions for ab initio protein structure prediction. Methods Mol. Biol., 143, 223–245.PubMedGoogle Scholar
  36. 36.
    Hartree, D.R. (1957) The Calculation of Atomic Structure. John Wiley & Sons, New York.Google Scholar
  37. 37.
    Hohenberg, P., Kohn, W. (1964) Inhomogeneous electron gas. Phys. Rev., 136, 864.CrossRefGoogle Scholar
  38. 38.
    Kauzmann, W. (1959) Some factors in the interpretation of protein denaturation. Adv. Protein Chem., 14, 1–64.PubMedCrossRefGoogle Scholar
  39. 39.
    Dill, K.A. (1990) Dominant forces in protein folding. Biochemistry, 29, 7133–7155.PubMedCrossRefGoogle Scholar
  40. 40.
    Morozov, A.V., Kortemme, T., Tsemekhman, K., Baker, D. (2004) Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations. Proc. Natl. Acad. Sci. U. S. A., 101, 6946–6951.PubMedCrossRefGoogle Scholar
  41. 41.
    Weiner, P.K., Kollman P.A. (1981) AMBER: Assisted model building with energy refinement. A general program for modeling molecules and their interactions. J. Comp. Chem., 2, 287–303.CrossRefGoogle Scholar
  42. 42.
    Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S., Karplus, M. (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comp. Chem., 4, 187–217.CrossRefGoogle Scholar
  43. 43.
    Levitt, M., Hirshberg, M., Sharon, R., Daggett, V. (1995) Potential energy function and parameters for simulations of the molecular dynamics of proteins and nucleic acids in solution. Comp. Phys. Comm., 91, 215–231.CrossRefGoogle Scholar
  44. 44.
    Levitt, M. (1983) Molecular dynamics of native protein. I. Computer simulation of trajectories. J. Mol. Biol., 168, 595–617.PubMedCrossRefGoogle Scholar
  45. 45.
    Daggett, L.P., Sacaan, A.I., Akong, M., Rao, S.P., Hess, S.D., Liaw, C., Urrutia, A., Jachec, C., Ellis, S.B., Dreessen J, et al. (1995) Molecular and functional characterization of recombinant human metabotropic glutamate receptor subtype 5. Neuropharmacology, 34, 7133–7155.CrossRefGoogle Scholar
  46. 46.
    Levitt, M. (1983) Protein folding by restrained energy minimization and molecular dynamics. J. Mol. Biol., 170, 723–764.PubMedCrossRefGoogle Scholar
  47. 47.
    Brunger, A.T., Clore, G.M., Gronenborn, A.M., Karplus, M. (1986) Three-dimensional structure of proteins determined by molecular dynamics with interproton distance restraints: application to crambin. Proc. Natl. Acad. Sci. U. S. A., 83, 3801–3805.PubMedCrossRefGoogle Scholar
  48. 48.
    Ferguson, D.M., Kollman, P.A. (1991) Can the Lennard-Jones 6-12 function replace the 10–12 form in molecular mechanics calculations? J. Comput. Chem., 12, 620–626.CrossRefGoogle Scholar
  49. 49.
    Halgren, T.A. (1992) Representation of van der Waals (vdW) interactions in molecular mechanics force fields: potential form, combination rules, and vdW parameters. J. Am. Chem. Soc., 114, 7827–7843.CrossRefGoogle Scholar
  50. 50.
    Halgren, T.A. (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem., 17, 490–519.CrossRefGoogle Scholar
  51. 51.
    Hart, J.R., Rappe, A.K. (1992) van der Waals functional forms for molecular simulations. J. Chem. Phys., 97, 1109–1115.CrossRefGoogle Scholar
  52. 52.
    Buckingham, A.D., Fowler, P.W. (1985) A model for the geometries of van der Waals complexes. Can. J. Chem., 63, 2018.CrossRefGoogle Scholar
  53. 53.
    Sokalski, W.A., Shibata, M., Ornstein, R.L., Rein, R. (1993) Point charge representation of multicenter multipole moments in calculation of electrostatic properties. Theor. Chim. Acta, 85, 209–216.PubMedCrossRefGoogle Scholar
  54. 54.
    Stone, A.J. (1981) Distributed multipole analysis, or how to describe a molecular charge distribution. Chem. Phys. Lett., 83, 233–239.CrossRefGoogle Scholar
  55. 55.
    Kosov, D., Popelier, P.L.A. (2000) Atomic partitioning of molecular electrostatic potentials. J. Phys. Chem. A, 104, 7339–7345.CrossRefGoogle Scholar
  56. 56.
    Cieplak, P., Caldwell, J., Kollman, P. (2001) Molecular mechanical models for organic and biological systems going beyond the atom centered two body additive approximation: aqueous solution free energies of methanol and N-methyl acetamide, nucleic acid base, and amide hydrogen bonding and chloroform/water partition coefficients of the nucleic acid bases. J. Comput. Chem., 22, 1048–1057.CrossRefGoogle Scholar
  57. 57.
    Kaminski, G.A., Stern, H.A., Berne, B.J., Friesner, R.A., Cao, Y.X., Murphy, R.B., Zhou, R., Halgren, T.A. (2002) Development of a polarizable force field for proteins via ab initio quantum chemistry: first generation model and gas phase tests. J. Comput. Chem., 23, 1515–1531.PubMedCrossRefGoogle Scholar
  58. 58.
    Ren, P., Ponder, J.W. (2003) Polarizable atomic multipole water model for molecular mechanics simulation. J. Phys. Chem. B, 107, 5933–5947.CrossRefGoogle Scholar
  59. 59.
    Jorgensen, W.L. (1981) Transferable intermolecular potential functions for water, alcohols, and ethers. Application to liquid water. J. Am. Chem. Soc., 103, 335–340.CrossRefGoogle Scholar
  60. 60.
    Jorgensen, W.L., Chandrasekhar, J., Madura, J.D., Impey, R.W., Klein, M.L. (1983) Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 79, 926–935.CrossRefGoogle Scholar
  61. 61.
    Berendsen, H.J.C., Grigera, J.R., Straatsma, T.P. (1987) The missing term in effective pair potentials. J. Phys. Chem., 91, 6269–6271.CrossRefGoogle Scholar
  62. 62.
    Levitt, M., Hirshberg, M., Sharon, R., Laidig, K.E., Daggett, V. (1997) Calibration and testing of a water model for simulation of the molecular dynamics of proteins and nucleic acids in solution. J. Phys. Chem. B, 101, 5051–5061.CrossRefGoogle Scholar
  63. 63.
    York, D.M., Darden, T., Pedersen, L.G. (1993) The effect of long-range electrostatic interactions in simulations of macromolecular crystals: a comparison of the Ewald and truncated list methods. J. Chem. Phys., 99, 8345–8348.CrossRefGoogle Scholar
  64. 64.
    Darden, T., York, D., Pedersen, L. (1993) Particle mesh Ewald: an N*log(N) method for Ewald sums in large systems J. Chem. Phys., 98, 10089–10092.CrossRefGoogle Scholar
  65. 65.
    Gouy, M. (1910) Sur la constitution de la charge èlectrique a la surface d’un électrolyte. Journ. Phys., 9, 457–468.Google Scholar
  66. 66.
    Gilson, M.K., Honig, B. (1988) Calculation of the total electrostatic energy of a macromolecular system: solvation energies, binding energies, and conformational analysis. Proteins, 4, 7–18.PubMedCrossRefGoogle Scholar
  67. 67.
    Nicholls, A., Honig, B. (1991) A rapid finite difference algorithm, utilizing successive over-relaxation to solve the Poisson-Boltzmann equation. J. Comp. Chem., 12, 435–445.CrossRefGoogle Scholar
  68. 68.
    Bashford, D., Case, D.A. (2000) Generalized Born models of macromolecular solvation effects. Annu. Rev. Phys. Chem., 51, 129–152.PubMedCrossRefGoogle Scholar
  69. 69.
    de Bakker, P.I.W., DePristo, M.A., Burke, D.F., Blundell, T.L. (2003) Ab initio construction of polypeptide fragments: accuracy of loop decoy discrimination by an all-atom statistical potential and the AMBER force field with the generalized born solvation model. Proteins, 51, 21–40.PubMedCrossRefGoogle Scholar
  70. 70.
    Fogolari, F., Brigo, A., Molinari, H. (2003) Protocol for MM/PBSA molecular dynamics simulations of proteins. Biophys. J., 85, 159–166.PubMedCrossRefGoogle Scholar
  71. 71.
    Warshel, A., Levitt, M. (1976) Theoretical studies of enzymic reactions – dielectric, electrostatic and steric stabilization of carbonium-ion in reaction of lysozyme. J. Mol. Biol., 103, 227–249.PubMedCrossRefGoogle Scholar
  72. 72.
    Gelin, B.R., Karplus, M. (1979) Side-chain torsional potentials: effect of dipeptide, protein, and solvent environment. Biochemistry, 18, 1256–1268.PubMedCrossRefGoogle Scholar
  73. 73.
    Lazaridis, T., Karplus, M. (1999) Effective energy function for proteins in solution. Proteins, 35, 133–152.PubMedCrossRefGoogle Scholar
  74. 74.
    Mallik, B., Masunov, A., Lazaridis, T. (2002) Distance and exposure dependent effective dielectric function. J. Comp. Chem., 23, 1090–1099.CrossRefGoogle Scholar
  75. 75.
    Moult, J. (1997) Comparison of database potentials and molecular mechanics force fields. Curr. Opin. Struct. Biol., 7, 194–199.PubMedCrossRefGoogle Scholar
  76. 76.
    Eisenberg, D., Weiss, R.M., Terwillinger, T.C. (1982) The helical hydrophobic moment: a measure of the amphiphilicity of a helix. Nature, 299, 371–374.PubMedCrossRefGoogle Scholar
  77. 77.
    Sippl, M.W., S. (1992) Detection of native-like models for amino acid sequences of unknown three-dimensional structure in a database of known protein conformations. Proteins, 13, 258–271.PubMedCrossRefGoogle Scholar
  78. 78.
    Jones, D.T. (2001) Predicting novel protein folds by using FRAGFOLD. Proteins, 45, 127–132.CrossRefGoogle Scholar
  79. 79.
    Zhang, Y., Skolnick, J. (2004) Tertiary structure predictions on a comprehensive benchmark of medium to large size proteins. Biophys. J., 87, 2647–2655.PubMedCrossRefGoogle Scholar
  80. 80.
    Boniecki, M., Rotkiewicz, P., Skolnick, J., Kolinski, A. (2003) Protein fragment reconstruction using various modeling techniques. J. Comput. Aided Mol. Des., 17, 725–738.PubMedCrossRefGoogle Scholar
  81. 81.
    Hung, L.H., Ngan, S.C., Liu, T., Samudrala, R. (2005) PROTINFO: new algorithms for enhanced protein structure predictions. Nucleic Acids Res., 33, W77–W80.PubMedCrossRefGoogle Scholar
  82. 82.
    Westbrook, J., Feng, Z., Chen, L., Yang, H., Berman, H.M. (2003) The Protein Data Bank and structural genomics. Nucleic Acids Res., 31, 489–491.PubMedCrossRefGoogle Scholar
  83. 83.
    Bourne, P.E., Addess, K.J., Bluhm, W.F., Chen, L., Deshpande, N., Feng, Z., Fleri, W., Green, R., Merino-Ott, J.C., Townsend-Merino, W., Weissig, H., Westbrook, J., Berman, H.M. (2004) The distribution and query systems of the RCSB Protein Data Bank. Nucleic Acids Res., 32, D223–D225.PubMedCrossRefGoogle Scholar
  84. 84.
    Chandonia, J.M., Hon, G., Walker, N.S., LoConte, L., Koehl, P., Levitt, M., Brenner, S.E. (2004) The ASTRAL compendium in 2004. Nucleic Acids Res., 32, D189–D192.PubMedCrossRefGoogle Scholar
  85. 85.
    Subramaniam, S., Tcheng, D.K., Fenton, J. (1996) Knowledge-based methods for protein structure refinement and prediction, in Proceedings of the Fourth International Conference on Intelligent Systems in Molecular Biology (States, D., Agarwal, P., Gaasterland, T., Hunter, L. & Simth, R., eds.), AAAI Press, Menlo Park, CA, pp. 218–229.Google Scholar
  86. 86.
    Avbelj, F., Moult, J. (1995) Role of electrostatic screening in determining protein main chain conformational preferences. Biochemistry, 34, 755–764.PubMedCrossRefGoogle Scholar
  87. 87.
    Lu, H., Skolnick, J. (2001) A distance-dependent atomic knowledge-based potential for improved protein structure selection. Proteins, 44, 223–232.PubMedCrossRefGoogle Scholar
  88. 88.
    Zhou, H., Zhou, Y. (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci., 11, 2714–2726.PubMedCrossRefGoogle Scholar
  89. 89.
    Oppenheim, A.V., Schafer, R.W., Buck, J.R. (1999) Discrete-Time Signal Processing, 2nd ed. Prentice Hall, Upper Saddle River, NJ.Google Scholar
  90. 90.
    Rost, B., Sander, C. (1994) Conservation and prediction of solvent accessibility in protein families. Proteins, 20, 216–226.PubMedCrossRefGoogle Scholar
  91. 91.
    Ahmad, S., Gromiha, M.M. (2002) NETASA: neural network based prediction of solvent accessibility. Bioinformatics, 18, 819–824.PubMedCrossRefGoogle Scholar
  92. 92.
    Kim, H., Park, H. (2004) Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor. Proteins, 54, 557–562.PubMedCrossRefGoogle Scholar
  93. 93.
    Rost, B., Sander, C. (1993) Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol., 232, 584–599.PubMedCrossRefGoogle Scholar
  94. 94.
    Jones, D.T. (1999) Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol., 292, 195–202.PubMedCrossRefGoogle Scholar
  95. 95.
    Cuff, J.A., Barton, G.J. (1999) Application of enhanced multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins, 40, 502–511.CrossRefGoogle Scholar
  96. 96.
    Lund, O., Frimand, K., Gorodkin, J., Bohr, H., Bohr, J., Hansen, J., Brunak, S. (1997) Protein distance constraints predicted by neural networks and probability density functions. Protein Eng., 10, 1241–1248.PubMedCrossRefGoogle Scholar
  97. 97.
    Pollastri, G., Baldi, P., Fariselli, P., Casadio, R. (2002) Prediction of coordination number and relative solvent accessibility in proteins. Proteins, 47, 142–153.PubMedCrossRefGoogle Scholar
  98. 98.
    Olmea, O., Valencia, A. (1997) Improving contact predictions by the combination of correlated mutations and other sources of sequence information. Fold Des., 2, S25–32.PubMedCrossRefGoogle Scholar
  99. 99.
    Fariselli, P., Casadio, R. (1999) Neural network based predictor of residue contacts in proteins. Protein Eng., 12, 15–21.PubMedCrossRefGoogle Scholar
  100. 100.
    Altschul, S.F., Madden, T.L., Schaffer, A.A. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 25, 3389–3402.PubMedCrossRefGoogle Scholar
  101. 101.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986) Learning representations by back-propagating errors. Nature, 323, 533–536.CrossRefGoogle Scholar
  102. 102.
    Yi, T.-M., Lander, E.S. (1993) Protein secondary structure prediction using nearest-neighbor methods. J. Mol. Biol., 232, 1117–1129.PubMedCrossRefGoogle Scholar
  103. 103.
    Kabsch, W., Sander, C. (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22, 2577–2637.PubMedCrossRefGoogle Scholar
  104. 104.
    Zell, A., Mamier, G., Vogt, M., et al. (2005) The SNNS users manual version 4.1. Available at http://www-ra.informatik.uni-tuebingen.de/snns.Google Scholar
  105. 105.
    Park, B., Levitt, M. (1996) Energy functions that discriminate x-ray and near native folds from well-constructed decoys. J. Mol. Biol., 266, 831–846.CrossRefGoogle Scholar
  106. 106.
    Novotny, J., Bruccoleri, R., Karplus, M. (1984) An analysis of incorrectly folded protein models. Implications for structure predictions. J. Mol. Biol., 177, 787–818.PubMedCrossRefGoogle Scholar
  107. 107.
    Holm, L., Sander, C. (1992) Evaluation of protein models by atomic solvation preference. J. Mol. Biol., 225, 93–105.PubMedCrossRefGoogle Scholar
  108. 108.
    Samudrala, R., Levitt, M. (2000) Decoys ‘R’ Us: a database of incorrect conformations to improve protein structure prediction. Protein Sci., 9, 1399–1401.PubMedCrossRefGoogle Scholar
  109. 109.
    Tsai J., B., R., Morozov, A.V., Kuhlman, B., Rohl, C.A., Baker, D. (2003) An improved protein decoy set for testing energy functions for protein structure prediction. Proteins, 53, 76–87.PubMedCrossRefGoogle Scholar
  110. 110.
    Park, B.H., Huang, E.S., Levitt, M. (1997) Factors affecting the ability of energy functions to discriminate correct from incorrect folds. J. Mol. Biol., 266, 831–846.PubMedCrossRefGoogle Scholar
  111. 111.
    Hinds, D.A., Levitt, M. (1992) A lattice model for protein structure prediction at low resolution. Proc. Natl. Acad. Sci. U. S. A., 89, 2536–2540.PubMedCrossRefGoogle Scholar
  112. 112.
    Park, B., Levitt, M. (1995) The complexity and accuracy of discrete state models of protein structure. J. Mol. Biol., 249, 493–507.PubMedCrossRefGoogle Scholar
  113. 113.
    Simons, K.T., Kooperberg, C., Huang, E., Baker, D. (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol., 268, 209–225.PubMedCrossRefGoogle Scholar
  114. 114.
    Hung, L.H., Samudrala, R. (2003) PROTINFO: secondary and tertiary protein structure prediction. Nucleic Acids Res., 31, 3296–3299.PubMedCrossRefGoogle Scholar
  115. 115.
    McConkey, B.J., Sobolev, V., Edelman, M. (2003) Discrimination of native protein structures using atom-atom contact scoring. Proc. Natl. Acad. Sci. U. S. A., 100, 3215–3220.PubMedCrossRefGoogle Scholar
  116. 116.
    Carter, C.W., Jr., LeFebvre, B.C., Cammer, S.A., Tropsha, A., Edgell, M.H. (2001) Four-body potentials reveal protein-specific correlations to stability changes caused by hydrophobic core mutations. J. Mol. Biol., 311, 625–638.PubMedCrossRefGoogle Scholar
  117. 117.
    Krishnamoorthy, B., Tropsha, A. (2003) Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations. Bioinformatics, 19, 1540–1548.PubMedCrossRefGoogle Scholar
  118. 118.
    Ngan, S.-C., Inonye, M.T, Samudrala, R. (2006) A knowledge-based scoring function based on residue triplets for protein structure prediction. Protein Eng., 19, 187–193.CrossRefGoogle Scholar
  119. 119.
    Li, X., Hu, C., Liang, J. (2003) Simplicial edge representation of protein structures and alpha contact potential with confidence measure. Proteins, 53, 792–805.PubMedCrossRefGoogle Scholar
  120. 120.
    Wang, K., Fain, B., Levitt, M., Samudrala, R. (2004) Improved protein structure selection using decoy-dependent discriminatory functions. BMC Struct. Biol., 4, 8.PubMedCrossRefGoogle Scholar
  121. 121.
    Zhang, Y., Skolnick, J. (2004) SPICKER: a clustering approach to identify near-native protein folds. J. Comput. Chem., 25, 865–871.PubMedCrossRefGoogle Scholar
  122. 122.
    Samudrala, R. (2006). RAMP Howto. Available at http://software.compbio. washington.edu/ramp/ramp.htmlGoogle Scholar
  123. 123.
    Misura, K.M.S., Baker, D. (2005) Progress and challenges in high-resolution refinement of protein structure models. Proteins, 59, 15–29.PubMedCrossRefGoogle Scholar
  124. 124.
    Bradley, P., Misura, K.M.S., Baker, D. (2005) Toward high-resolution de novo structure prediction for small proteins. Science, 309, 1868–1871.PubMedCrossRefGoogle Scholar
  125. 125.
    Kortemme, T., Morozov, A.V., Baker, D. (2003) An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. J. Mol. Biol., 326, 1239–1259.PubMedCrossRefGoogle Scholar
  126. 126.
    Bonneau, R., Ruczinski, I., Tsai, J., Baker, D. (2002) Contact order and ab initio protein structure prediction. Protein Sci., 11, 1937–1944.PubMedCrossRefGoogle Scholar
  127. 127.
    Bradley, P., Malmstrom, L., Qian, B., Schonburn, J., Chivian, D., Kim, D.E., Meiler, J., Misura, K.M., Baker D. (2005) Free modeling with Rosetta in CASP6. Proteins, 61, 128–134.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc 2008

Authors and Affiliations

  • Shing-Chung Ngan
  • Ling-Hong Hung
  • Tianyun Liu
  • Ram Samudrala

There are no affiliations available

Personalised recommendations