Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Separators for sphere-packings and nearest neighbor graphs
Journal of the ACM (JACM)
On the approximation of protein threading
Theoretical Computer Science - Special issue: Genome informatics
Side Chain-Positioning as an Integer Programming Problem
WABI '01 Proceedings of the First International Workshop on Algorithms in Bioinformatics
A Semidefinite Programming Approach to Side Chain Positioning with New Rounding Strategies
INFORMS Journal on Computing
Rapid protein side-chain packing via tree decomposition
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Efficient Parameterized Algorithms for Biopolymer Structure-Sequence Alignment
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Graph algorithms for biological systems analysis
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Boosting Protein Threading Accuracy
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Efficient recognition of acyclic clustered constraint satisfaction problems
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
Tree decompositions of graphs: Saving memory in dynamic programming
Discrete Optimization
How accurately can we model protein structures with dihedral angles?
WABI'12 Proceedings of the 12th international conference on Algorithms in Bioinformatics
D-flat: Declarative problem solving using tree decompositions and answer-set programming
Theory and Practice of Logic Programming
Block Local Elimination Algorithms for Sparse Discrete Optimization Problems
Cybernetics and Systems Analysis
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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 0( \geqslant \left| V \right|^{\frac{2}{3}} \log \left| V \right|). The computational complexity of the tree-decomposition based graph labeling algorithms is 0(\left| V \right|\Delta ^{tw + 1}) where 驴 is the average number of possible labels for each vertex and tw( = 0(\left| V \right|^{\frac{2}{3}} \log \left| V \right|)) 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 moreefficient than the linear programming approach. Experimental result indicates that the tree-decomposition approach is more efficient most of the time.