A tree-decomposition approach to protein structure prediction

Proc IEEE Comput Syst Bioinform Conf. 2005;247-56. doi: 10.1109/csb.2005.9.


This paper proposes a tree decomposition of protein structures, which can be used to efficiently solve two key subproblems of protein structure prediction: protein threading for backbone prediction and protein side-chain prediction. To develop a unified tree-decomposition based approach to these two subproblems, we model them as a geometric neighborhood graph labeling problem. Theoretically, we can have a low-degree polynomial time algorithm to decompose a geometric neighborhood graph G = (V, E) into components with size O(|V|((2/3))log|V|). The computational complexity of the tree-decomposition based graph labeling algorithms is O(|V|Delta(tw+1)) where Delta is the average number of possible labels for each vertex and tw( = O(|V|((2/3))log|V|)) the tree width of G. Empirically, tw is very small and the tree-decomposition method can solve these two problems very efficiently. This paper also compares the computational efficiency of the tree-decomposition approach with the linear programming approach to these two problems and identifies the condition under which the tree-decomposition approach is more efficient than the linear programming approach. Experimental result indicates that the tree-decomposition approach is more efficient most of the time.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Amino Acid Sequence
  • Computer Simulation
  • Models, Chemical*
  • Models, Molecular*
  • Molecular Sequence Data
  • Protein Conformation
  • Proteins / chemistry*
  • Proteins / ultrastructure*
  • Sequence Analysis, Protein / methods*


  • Proteins