Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a new algorithm for protein structure alignment in the case where a protein structure is represented by a contact map graph and the sequential order of residues is strictly enforced in the alignment. The time complexity of the algorithm, O(n(2n+w)!/((2n)!w!)) where n is the maximum number of residues in two proteins and w = O((D u /D l ) n 2/3  log n) is the treewidth of a contact graph. In particular, D u  is the contact distance cutoff and D c  is the minimum inter-residue distance in a typical protein. If the distance beten two matched residues after two proteins are superimposed is bounded by a parameter D c , then the time complexity is O((n/epsivD c ) 6 ((n+w)!/(n!w!)Delta w )) where e is a small constant and Delta = (1 + (2D c /D l )) 3 . This algorithm consists of two major components: tree-decomposition and a DEE-like pruning strategy. The tree-decomposition method exploits the geometric feature of a protein structure and guarantees to terminate within subexponential time. The DEE-like pruning strategy exploits the sequential constraint and can quickly eliminate non-optimal alignments from a large search space. Based on these two techniques,  have developed a structure alignment program TreeAlign to find the globally optimal solution of the contact map overlap problem. The preliminary experimental results indicate that on a Linux PC with 2.5 GHz CPU, TreeAlign can align two similar proteins within seconds and two dissimilar proteins within minutes.
['Jinbo Xu']
I am writing a research paper on a idea Solving the contact map overlap problem via tree decomposition and a DEE-like pruning strategy
[{"from": "human", "value": "I am writing a research paper on a idea Solving the contact map overlap problem via tree decomposition and a DEE-like pruning strategy"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a new algorithm for protein structure alignment in the case where a protein structure is represented by a contact map graph and the sequential order of residues is strictly enforced in the alignment. The time complexity of the algorithm, O(n(2n+w)!/((2n)!w!)) where n is the maximum number of residues in two proteins and w = O((D u /D l ) n 2/3  log n) is the treewidth of a contact graph. In particular, D u  is the contact distance cutoff and D c  is the minimum inter-residue distance in a typical protein. If the distance beten two matched residues after two proteins are superimposed is bounded by a parameter D c , then the time complexity is O((n/epsivD c ) 6 ((n+w)!/(n!w!)Delta w )) where e is a small constant and Delta = (1 + (2D c /D l )) 3 . This algorithm consists of two major components: tree-decomposition and a DEE-like pruning strategy. The tree-decomposition method exploits the geometric feature of a protein structure and guarantees to terminate within subexponential time. The DEE-like pruning strategy exploits the sequential constraint and can quickly eliminate non-optimal alignments from a large search space. Based on these two techniques,  have developed a structure alignment program TreeAlign to find the globally optimal solution of the contact map overlap problem. The preliminary experimental results indicate that on a Linux PC with 2.5 GHz CPU, TreeAlign can align two similar proteins within seconds and two dissimilar proteins within minutes."}]