Almost FPRAS for Lattice Models of Protein Folding

(Extended Abstract)
  • Anna Gambin
  • Damian Wójtowicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3503)


The provably efficient randomized approximation scheme which evaluates the partition function for the wide class of lattice models of protein prediction is presented. We propose to apply the idea of self-testing algorithms introduced recently in [8]. We consider the protein folding process which is simplified to a self-avoiding walk on a lattice. The power of a simplified approach is in its ability to search the conformation space, to train the search parameters and to test basic assumptions about the nature of the protein folding process. Our main theoretical results are formulated in the general setting, i.e. we do not assume any specific lattice model. For the simulation study we have chosen the HP model on the FCC lattice.


Markov Chain Partition Function Lattice Model Protein Structure Prediction Markov Chain Monte Carlo Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Dill, K.A., Bromerg, S., Yue, K., Fiebig, K.M., Yee, P.D., Thomas, P.D., Chan, H.S.: Principles of protein folding – A perspective from simple exact models. Protein Science 4, 561–602 (1995)CrossRefGoogle Scholar
  2. 2.
    Jerrum, M.R., Sinclair, A.J.: Approximating the permanent. SIAM Journal on Computing 18, 1149–1178 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Madras, N., Slade, G.: The Self-Avoiding Walk, Birkhäuser, Boston (1993)Google Scholar
  4. 4.
    Nayak, A., Sinclair, A., Zwick, U.: Spatial Codes and the Hardness of String Folding. Journal of Computational Biology 6(1), 13–36 (1999)CrossRefGoogle Scholar
  5. 5.
    Niemiro, W., Pokarowski, P.: Faster MCMC Estimation Along One Walk (2001) (Preprint)Google Scholar
  6. 6.
    Pokarowski, P., Koliński, A., Skolnik, J.: A minimal physically realistic protein-like lattice model: Designing an enargy landscape that ensures all-or-one folding to a unique native state. Biophysical Journal (2003) (in press)Google Scholar
  7. 7.
    Randall, D.: Counting in lattices: combinatorial problems from statistical mechanics. PhD Thesis, UC Berkeley (1995)Google Scholar
  8. 8.
    Randall, D., Sinclair, A.J.: Self-Testing Algorithms for Self-Avoiding Walks. Journal of Mathematical Physics 41, 1570–1584 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Schuster, P., Stadler, P.F.: Discrete Models of Biopolymers (1999) (TBI Preprint)Google Scholar
  10. 10.
    Sinclair, A.J.: Algorithms for random generation and counting: a Markov chain approach, Birkhäuser, Boston (1992)Google Scholar
  11. 11.
    Will, S.: Constraint-based hydrophobic core construction for protein structure prediction in the face-centered-cubic lattice. In: Proc. Pacific Symposium on Biocomputing (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Anna Gambin
    • 1
  • Damian Wójtowicz
    • 1
  1. 1.Institute of InformaticsWarsaw UniversityWarsawPoland

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