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Advances in Bioinformatics
Volume 2014, Article ID 867179, 9 pages
http://dx.doi.org/10.1155/2014/867179
Research Article

How Good Are Simplified Models for Protein Structure Prediction?

1Institute for Integrated and Intelligent Systems (IIIS), Griffith University, 170 Kessels Road, Nathan, QLD 4111, Australia
2Queensland Research Laboratory, National ICT of Australia (NICTA), GPO Box 2434, Brisbane, QLD 4001, Australia

Received 31 October 2013; Revised 22 January 2014; Accepted 23 January 2014; Published 29 April 2014

Academic Editor: Bhaskar Dasgupta

Copyright © 2014 Swakkhar Shatabda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. B. Anfinsen, “Principles that govern the folding of protein chains,” Science, vol. 181, no. 4096, pp. 223–230, 1973. View at Google Scholar · View at Scopus
  2. B. H. Park and M. Levitt, “The complexity and accuracy of discrete state models of protein structure,” Journal of Molecular Biology, vol. 249, no. 2, pp. 493–507, 1995. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Mann, R. Saunders, C. Smith, R. Backofen, and C. M. Deane, “Producing high-accuracy lattice models from protein atomic coordinates including side chains,” Advances in Bioinformatics, vol. 2012, Article ID 148045, 6 pages, 2012. View at Publisher · View at Google Scholar
  4. S. Istrail and F. Lam, “Combinatorial algorithms for protein folding in lattice models: a survey of mathematical results,” Communications in Information and Systems, vol. 9, no. 4, article 303, 2009. View at Google Scholar
  5. L. Mirny and E. Shakhnovich, “Protein folding theory: from lattice to all-atom models,” Annual Review of Biophysics and Biomolecular Structure, vol. 30, no. 1, pp. 361–396, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Zhang, A. K. Arakaki, and J. Skolnick, “TASSER: an automated method for the prediction of protein tertiary structures in CASP6,” Proteins, vol. 61, no. 7, pp. 91–98, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Zhang, “I-TASSER: fully automated protein structure prediction in CASP8,” Proteins, vol. 77, no. 9, pp. 100–113, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Xu, J. Zhang, A. Roy, and Y. Zhang, “Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement,” Proteins, vol. 79, no. 10, pp. 147–160, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. P. Rotkiewicz and J. Skolnick, “Fast procedure for reconstruction of full-atom protein models from reduced representations,” Journal of Computational Chemistry, vol. 29, no. 9, pp. 1460–1465, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Fiser and A. Šali, “MODELLER: generation and refinement of homology-based protein structure models,” Methods in Enzymology, vol. 374, pp. 461–491, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Kolinski and J. Skolnick, “Reduced models of proteins and their applications,” Polymer, vol. 45, no. 2, pp. 511–524, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Moreno-Hernández and M. Levitt, “Comparative modeling and protein-like features of hydrophobic-polar models on a two-dimensional lattice,” Proteins, vol. 80, no. 6, pp. 1683–1693, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. K. F. Lau and K. A. Dill, “Lattice statistical mechanics model of the conformational and sequence spaces of proteins,” Macromolecules, vol. 22, no. 10, pp. 3986–3997, 1989. View at Google Scholar · View at Scopus
  14. S. Miyazawa and R. L. Jernigan, “Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation,” Macromolecules, vol. 18, no. 3, pp. 534–552, 1985. View at Google Scholar · View at Scopus
  15. E. Bornberg-Bauer, “Chain growth algorithms for HP-type lattice proteins,” in Proceedings of the 1st Annual International Conference on Computational Molecular Biology (RECOMB '97), pp. 47–55, ACM, January 1997. View at Scopus
  16. M. Berrera, H. Molinari, and F. Fogolari, “Amino acid empirical contact energy definitions for fold recognition in the space of contact maps,” BMC Bioinformatics, vol. 4, article 8, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Godzik, A. Kolinski, and J. Skolnick, “Lattice representations of globular proteins: how good are they?” Journal of Computational Chemistry, vol. 14, no. 10, pp. 1194–1202, 1993. View at Google Scholar
  18. G. Wang and R. L. Dunbrack Jr., “PISCES: recent improvements to a PDB sequence culling server,” Nucleic Acids Research, vol. 33, supplement 2, pp. W94–W98, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. J. R. Banavar, T. X. Hoang, F. Seno, A. Trovato, and A. Maritan, “Protein sequence and structure: is one more fundamental than the other?” Journal of Statistical Physics, vol. 148, no. 4, pp. 636–645, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Cieplak and J. R. Banavar, “Energy landscape and dynamics of proteins: an exact analysis of a simplified lattice model,” Physical Review E, vol. 88, no. 4, Article ID 040702, 2013. View at Google Scholar
  21. J. Mañuch; and D. R. Gaur, “Fitting protein chains to cubic lattice is NP-complete,” Journal of Bioinformatics and Computational Biology, vol. 6, no. 1, pp. 93–106, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. D. G. Covell and R. L. Jernigan, “Conformations of folded proteins in restricted spaces,” Biochemistry, vol. 29, no. 13, pp. 3287–3294, 1990. View at Google Scholar · View at Scopus
  23. D. A. Hinds and M. Levitt, “A lattice model for protein structure prediction at low resolution,” Proceedings of the National Academy of Sciences of the United States of America, vol. 89, no. 7, pp. 2536–2540, 1992. View at Google Scholar · View at Scopus
  24. J. Miao, J. Klein-Seetharaman, and H. Meirovitch, “The optimal fraction of hydrophobic residues required to ensure protein collapse,” Journal of Molecular Biology, vol. 344, no. 3, pp. 797–811, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Ponty, R. Istrate, E. Porcelli, and P. Clote, “LocalMove: computing on-lattice fits for biopolymers,” Nucleic Acids Research, vol. 36, pp. W216–W222, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. B. A. Reva, D. S. Rykunov, A. V. Finkelstein, and J. Skolnick, “Optimization of protein structure on lattices using a self-consistent field approach,” Journal of Computational Biology, vol. 5, no. 3, pp. 531–538, 1998. View at Google Scholar · View at Scopus
  27. B. A. Reva, D. S. Rykunov, A. J. Olson, and A. V. Finkelstein, “Constructing lattice models of protein chains with side groups,” Journal of Computational Biology, vol. 2, no. 4, pp. 527–535, 1995. View at Google Scholar · View at Scopus
  28. M. Mann and A. Dal Palu, “Lattice model refinement of protein structures,” in Proceedings of the Workshop on Constraint Based Methods for Bioinformatics (WCB '10), p. 7, 2010.
  29. M. A. Rashid, M. T. Hoque, M. A. Hakim Newton, D. Nghia Pham, and A. Sattar, “A new genetic algorithm for simplified protein structure prediction,” in AI 2012: Advances in Artificial Intelligence, pp. 107–119, Springer, 2012. View at Google Scholar
  30. I. Dotu, M. Cebrián, P. Van Hentenryck, and P. Clote, “On lattice protein structure prediction revisited,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1620–1632, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. A. D. Ullah and K. Steinhöfel, “A hybrid approach to protein folding problem integrating constraint programming with local search,” BMC Bioinformatics, vol. 11, no. 1, article S39, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. A. A. Albrecht, A. Skaliotis, and K. Steinhöfel, “Stochastic protein folding simulation in the three-dimensional HP-model,” Computational Biology and Chemistry, vol. 32, no. 4, pp. 248–255, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. K. Steinhofel, A. Skaliotis, and A. Albrecht, “Stochastic protein folding simulation in the d-dimensional hp-model,” in Bioinformatics Research and Development, pp. 381–394, Springer, 2007. View at Google Scholar
  34. S. Shatabda, M. A. Newton, D. N. Pham, and A. Sattar, “Memory-based local search for simplified protein structure prediction,” in Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, pp. 345–352, ACM, 2012.
  35. S. Shatabda, M. A. Hakim Newton, M. A. Rashid, D. N. Pham, and A. Sattar, “The road not taken: retreat and diverge in local search for simplified protein structure prediction,” BMC Bioinformatics, vol. 14, no. 2, pp. 1–9, 2013. View at Google Scholar
  36. C. Micheletti, F. Seno, J. R. Banavar, and A. Maritan, “Learning effective amino acid interactions through iterative stochastic techniques,” Proteins, vol. 42, no. 3, pp. 422–431, 2001. View at Google Scholar
  37. M. Vendruscolo, R. Najmanovich, and E. Domany, “Can a pairwise contact potential stabilize native protein folds against decoys obtained by threading?” Proteins, vol. 38, no. 2, pp. 134–148, 2000. View at Google Scholar
  38. M. Vendruscolo, “Assessment of the quality of energy functions for protein folding by using a criterion derived with the help of the noisy Go model,” Journal of Biological Physics, vol. 27, no. 2-3, pp. 205–215, 2001. View at Publisher · View at Google Scholar · View at Scopus
  39. K. Wang, B. Fain, M. Levitt, and R. Samudrala, “Improved protein structure selection using decoy-dependent discriminatory functions,” BMC Structural Biology, vol. 4, article 8, pp. 1–18, 2004. View at Publisher · View at Google Scholar · View at Scopus
  40. B. Cipra, “Packing challenge mastered at last,” Science, no. 5381, Article ID 1267, p. 281, 1998. View at Google Scholar
  41. S. Shatabda, M. A. Hakim Newton, D. N. Pham, and A. Sattar, “A hybrid local search for simplified protein structure prediction,” in Proceedings of the 4th International Conference on Bioinformatics Models, Methods and Algorithms (BIOIN-FORMATICS '13), BIOSTEC, Barcelona, Spain, 2013.
  42. N. Lesh, M. Mitzenmacher, and S. Whitesides, “A complete and effective move set for simplified protein folding,” in Proceedings of the 7th Annual International Conference on Research in Computational Molecular Biology, pp. 188–195, ACM, April 2003. View at Scopus
  43. M. A. Hakim Newton, D. N. Pham, A. Sattar, and M. J. Maher, “Kangaroo: an efficient constraint-based local search system using lazy propagation,” in Proceedings of the 17th International Conference on Principles and Practice of Constraint Programming (CP '1), pp. 645–659, 2011.
  44. S. Shatabda, M. A. Hakim Newton, and Abdul Sattar, “Simplified lattice models for protein structure prediction: how good are they?” in Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI Press, 2013.