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Abstract
BackgroundProtein structure comparison and classification is an effective method for exploring protein structure-function relations. This problem is computationally challenging. Many different computational approaches for protein structure comparison apply the secondary structure elements (SSEs) representation of protein structures.ResultsWe study the complexity of the protein structure comparison problem based on a mixed-graph model with respect to different computational frameworks. We develop an effective approach for protein structure comparison based on a novel independent set enumeration algorithm. Our approach (named: ePC, efficient enumeration-based Protein structure Comparison) is tested for general purpose protein structure comparison as well as for specific protein examples. Compared with other graph-based approaches for protein structure comparison, the theoretical running-time O(1.47rnn2) of our approach ePC is significantly better, where n is the smaller number of SSEs of the two proteins, r is a parameter of small value.ConclusionThrough the enumeration algorithm, our approach can identify different substructures from a list of high-scoring solutions of biological interest. Our approach is flexible to conduct protein structure comparison with the SSEs in sequential and non-sequential order as well. Supplementary data of additional testing and the source of ePC will be available at http://bioinformatics.astate.edu/.
Highlights
Protein structure comparison and classification is an effective method for exploring protein structurefunction relations
Protein structure comparison is an effective method for exploring protein structure-function relations and for studying evolutionary relations of different species
A mixed graph for a protein structure is constructed from the PDB file as follows: each vertex represents a core/secondary structure element, each undirected edge represents the interaction between two cores, and each directed edge represents the loop between two consecutive cores
Summary
Protein structure comparison and classification is an effective method for exploring protein structurefunction relations. The computational methods for protein structure comparison usually represent a protein structure by atomic coordinates in the Euclidean space, as a distance matrix [1] whose entries represent the distances between two residues of the protein, or as a contact map [2], where a binary matrix is used to represent the distances [3] for protein structure prediction. In this current work, we adopt the structure graph representation in [3]. Our approach transforms the comparison problem to an independent set problem in an auxiliary graph, and applies a novel enumeration algorithm to identify the best out of a set of good comparison candidates
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