Geometric Hashing: An Overview
IEEE Computational Science & Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Towards Index-based Similarity Search for Protein Structure Databases
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
PSIST: Indexing Protein Structures Using Suffix Trees
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
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Protein structure similarity and classification methods have many applications in protein function prediction and associated fields (e.g. drug discovery). In this paper, we propose a new protein structure representation method enabling fast and accurate classification. In our approach, each protein structure is represented by number of vectors (based on histogram of distances) equivalent to the number of its Cα residues. Each Cα residue represents a viewpoint from which the distances to each of the other residues are computed. Consequently, we use several methods to convert these distances into a n-dimensional feature vector which is indexed using a metric indexing structure (M-tree is the structure of our choice). While searching, we use single or multi-step approach which provides us with classification accuracy and speed comparable to the best contemporary classification methods.