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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)

Abstract

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.

Keywords

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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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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