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Hypergraph Model of Multi-residue Interactions in Proteins: Sequentially–Constrained Partitioning Algorithms for Optimization of Site-Directed Protein Recombination

  • Xiaoduan Ye
  • Alan M. Friedman
  • Chris Bailey-Kellogg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)

Abstract

Relationships among amino acids determine stability and function and are also constrained by evolutionary history. We develop a probabilistic hypergraph model of residue relationships that generalizes traditional pairwise contact potentials to account for the statistics of multi-residue interactions. Using this model, we detected non-random associations in protein families and in the protein database. We also use this model in optimizing site-directed recombination experiments to preserve significant interactions and thereby increase the frequency of generating useful recombinants. We formulate the optimization as a sequentially-constrained hypergraph partitioning problem; the quality of recombinant libraries wrt a set of breakpoints is characterized by the total perturbation to edge weights. We prove this problem to be NP-hard in general, but develop exact and heuristic polynomial-time algorithms for a number of important cases. Application to the beta-lactamase family demonstrates the utility of our algorithms in planning site-directed recombination.

Keywords

Edge Weight Protein Structure Prediction Stochastic Dynamic Programming Additional Perturbation Potential Score 
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 2006

Authors and Affiliations

  • Xiaoduan Ye
    • 1
  • Alan M. Friedman
    • 2
  • Chris Bailey-Kellogg
    • 1
  1. 1.Department of Computer ScienceDartmouth CollegeHanoverUSA
  2. 2.Department of Biological Sciences and Purdue Cancer CenterPurdue UniversityWest LafayetteUSA

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