Artificial Intelligence in Medicine
Volume 39, Issue 1 , Pages 49-63 , January 2007

Side chain placement using estimation of distribution algorithms

Received 21 December 2005 ,Revised 26 April 2006 ,Accepted 28 April 2006.

References 

  1. Al-Lazikani B, Jung J, Xiang Z, Honig B. Protein structure prediction. Curr Opin Chem Biol. 2001;5(1):51–56
  2. Dill KA. Theory for the folding and stability of globular proteins. Biochemistry. 1985;24(6):1501–1509
  3. Kolinski A, Skolnick J. Reduced models of proteins and their applications. Polymer. 2004;45(2):511–524
  4. Mongea A, Lathropa EJP, Gunna JR, Shenkina PS, Freisnera RA. Computer modeling of protein folding: conformational and energetic analysis of reduced and detailed protein models. J Mol Biol. 1995;247(5):995–1012
  5. Dunbrack RL. Rotamer libraries in the 21st century. Curr Opin Struct Biol. 2002;12:431–440
  6. Kraemer-Pecore CM, Wollacott AM, Desjarlais JR. Computational protein design. Curr Opin Chem Biol. 2001;5:690–695
  7. Lazar GA, Desjarlais JR, Handel TM. De novo protein design of the hydrophobic core of ubiquitin. Protein Sci. 1997;6:1167–1178
  8. Rohl CA, Strauss CEM, Misura K, Baker D. Protein structure prediction using Rosetta. Methods Enzymol. 2004;383:66–93
  9. Dandekar T, Köenig R. Computational methods for the prediction of protein folds. Biochim Biophys Acta (BBA) - Protein Struct Mol Enzymol. 1997;1343(1):1–15
  10. Canutescu AA, Shelenkov AA, Dunbrack RL. A graph-theory algorithm for rapid protein side-chain prediction. Protein Sci. 2003;12:2001–2014
  11. Liang S, Grishin NV. Side-chain modeling with an optimized scoring function. Protein Sci. 2002;11:322–331
  12. Liu Z, Li W, Liang S, Han Y, Lai L. Beyond rotamer library: genetic algorithm combined with disturbing mutation process for upbuilding protein side-chains. Proteins: Structure, Funct Genet. 2003;50:49–62
  13. Pokala N, Handel TM. Review: protein design—where we were, where we are, where we’re going. J Struct Biol. 2001;134:269–281
  14. Lee C, Subbiah S. Prediction of protein side-chain conformation by packing optimization. J Mol Biol. 1991;217:373–388
  15. Shenkin PS, Farid H, Fetrow JS. Prediction and evaluation of side-chain conformations for protein backbone structures. Proteins: Structure, Funct Genet. 1998;26(3):323–352
  16. Vasquez M. Modeling side-chain conformation. Curr Opin Struct Biol. 1996;6(2):217–221
  17. Pierce NA, Winfree E. Protein design is NP-hard. Protein Eng. 2002;15(10):779–782
  18. Ponder JW, Richard FM. Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequence for different structure classes. J Mol Biol. 1987;193:775–791
  19. In:  Larrañaga P,  Lozano JA editor. Estimation of distribution algorithms. A new tool for evolutionary computation. Boston/Dordrecht/London: Kluwer Academic Publishers; 2002;
  20. H. Mühlenbein, G. Paaß, From recombination of genes to the estimation of distributions. I. Binary parameters. In: Eiben AE, Bäck T, Schoenauer M, Schwefel HP, editors. parallel problem solving from nature—PPSN IV, ser. Lecture Notes in Computer Science, vol. 1141. Berlin: Springer Verlag; 1996. p. 178–87.
  21. Goldberg DE. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley; 1989;
  22. Holland JH. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press; 1975;
  23. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleid Acid Res. 2000;28:235–242
  24. Tuffery P, Etchebest C, Hazout S, Lavery R. A new approach to the rapid determination of protein side chain conformations. J Biomol Struct Dynam. 1991;8:1267–1289
  25. Dunbrack RL, Cohen FE. Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci. 1997;6(8):1661–1681
  26. Wernisch L, Hery S, Wodak S. Automatic protein design with all atom force-fields by exact and heuristic optimization. J Mol Biol. 2000;301(3):713–736
  27. Voigt CA, Gordon DB, Mayo SL. Trading accuracy for speed: a quantitative comparison of search algorithms in protein sequence design. J Mol Biol. 2000;299(3):799–803
  28. Yanover C, Weiss Y. Approximate inference and protein-folding. In:  Becker S,  Thrun S,  Obermayer K editor. Advances in neural information processing systems 15. Cambridge, MA: MIT Press; 2003;p. 1457–1464
  29. Yanover C, Weiss Y, Approximate inference for side-chain prediction, submitted to Neural Computation journal 2004.
  30. De Maeyer M, Desmet J, Lasters I. The dead-end elimination theorem: Mathematical aspects, implementation, optimizations, evaluation, and performance. Methods Mol Biol. 2000;143:265–304
  31. Koehl P, Delarue M. Building protein lattice models using self consistent mean field theory. J Chem Phys. 1998;108:9540–9549
  32. R. Blanco, P. Larrañaga, I. Inza, and B. Sierra, Selection of highly accurate genes for cancer classification by estimation of distribution algorithms. In: Lucas P, van der Gaag L, Abu-Hamma A, editors, Proceedings of the Workshop Bayesian Models in Medicine held within AIME 2001; 2001. p. 29–34 [online]. Available: http://www.csd.abdn.ac.uk/∼plucas/bayesian.pdf (accessed: 7 April 2006).
  33. Peña JM, Lozano JA, Larrañaga P. Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering. Int J Uncertainty Fuzziness Knowledge-Based Syst. 2004;12(1):63–82
  34. Saeys Y, Degroeve S, Aeyels D, Rouzé P, Van de Peer Y. Feature selection for splice site prediction: a new method using EDA-based feature ranking. BMC Bioinformat. 2004;5:64–75
  35. Saeys Y, Degroeve S, Aeyels D, Van de Peer Y, Rouzé P. Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction. Bioinformatics. 2003;19(2):ii179–ii188
  36. Santana R, Larrañaga P, Lozano JA, Protein folding in two-dimensional lattices with estimation of distribution algorithms, In: Proceedings of the First International Symposium on Biological and Medical Data Analysis, ser. Lecture Notes in Computer Science, vol. 3337. Barcelona, Spain: Springer Verlag; 2004. p. 388–98.
  37. Larrañaga P. An introduction to probabilistic graphical models. In:  Larrañaga P,  Lozano JA editor. Estimation of distribution algorithms. A new tool for evolutionary computation. Boston/Dordrecht/London: Kluwer Academic Publishers; 2002;p. 25–54
  38. S. Baluja, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Pittsburgh, PA: Carnegie Mellon University. Tech. Re CMU-CS-94–163; 1994.
  39. Harik GR, Lobo FG, Goldberg DE. The compact genetic algorithm. IEEE Transact Evolut Computat. 1999;3(4):287–297
  40. Mühlenbein H, Mahnig T, Ochoa A. Schemata, distributions and graphical models in evolutionary optimization. J Heuristics. 1999;5(2):213–247
  41. De Bonet JS, Isbell CL, Viola P. MIMIC: finding optima by estimating probability densities. In:  Mozer MC,  Jordan MI,  Petsche T editor. Advances in neural information processing systems. vol. 9:Cambridge: The MIT Press; 1997;p. 424
  42. G. Harik, Linkage learning via probabilistic modeling in the EcGA. Urbana, IL: University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, IlliGAL Report No. 99010; 1999.
  43. Etxeberria R, Larrañaga P. Global optimization using Bayesian networks. In:  Ochoa A,  Soto MR,  Santana R editor. Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99). Havana, Cuba. March 1999;p. 151–173
  44. Mühlenbein H, Mahnig T. Evolutionary synthesis of Bayesian networks for optimization. Advances in Evolutionary Synthesis of Neural Systems. MIT Press; 2001;p. 429–55
  45. Pelikan M, Goldberg DE, Cantú-Paz E. BOA: The Bayesian optimization algorithm. In:  Banzhaf W,  Daida J,  Eiben AE,  Garzon MH,  Honavar V,  Jakiela M,  Smith RE editor. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99. vol. I. Orlando, FL. San Francisco, CA: Morgan Kaufmann Publishers; 1999;p. 525–532
  46. Mühlenbein H, Mahnig T. Evolutionary computation and beyond. In:  Uesaka Y,  Kanerva P,  Asoh H editor. Foundations of real-world intelligence. Stanford, California: CSLI Publications; 2001;p. 123–188
  47. Wales DJ, Scheraga HA. Global optimization of clusters, crystals, and biomolecules. Science. 1999;285(5432):1368–1372
  48. González C, Lozano JA, Larrañaga P. Mathematical modeling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions. Int J Approx Reason. 2002;31(4):313–340
  49. Glick M, Rayan A, Goldblum A. A stochastic algorithm for global optimization for best populations: a test case of side chains in proteins. Proc Natl Acad Sci. 2002;99(2):703–708
  50. Mühlenbein H, Höns R. The estimation of distributions and the minimum relative entropy principle. Evolut Computat. 2005;13(1):1–27

PII: S0933-3657(06)00062-5

doi: 10.1016/j.artmed.2006.04.004

Artificial Intelligence in Medicine
Volume 39, Issue 1 , Pages 49-63 , January 2007