Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
Database indexing for large DNA and protein sequence collections
The VLDB Journal — The International Journal on Very Large Data Bases
An Efficient Index-based Protein Structure Database Searching Method
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
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
OASIS: an online and accurate technique for local-alignment searches on biological sequences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Structure-based querying of proteins using wavelets
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
PSIST: A scalable approach to indexing protein structures using suffix trees
Journal of Parallel and Distributed Computing
Neural Network Method for Protein Structure Search Using Cell-Cell Adhesion
Neural Information Processing
PSISA: an algorithm for indexing and searching protein structure using suffix arrays
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
A hybrid approach for indexing and searching protein structures
WSEAS Transactions on Computers
DDPIn: distance and density based protein indexing
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Geometric suffix tree: Indexing protein 3-D structures
Journal of the ACM (JACM)
IEEE Transactions on Information Technology in Biomedicine
Geometric suffix tree: a new index structure for protein 3-d structures
CPM'06 Proceedings of the 17th Annual conference on Combinatorial Pattern Matching
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Approaches for indexing proteins, and for fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this paper, we developed a new method for extracting the local feature vectors of protein structures. Each residue is represented by a triangle, and the correlation between a set of residues is described by the distances between C_驴 atoms and the angles between the normals of planes in which the triangles lie. The normalized local feature vectors are indexed using a suffix tree. For all query segments, suffix trees can be used effectively to retrieve the maximal matches, which are then chained to obtain alignments with database proteins. Similar proteins are selected by their alignment score against the query. Our results shows classification accuracy up to 97.8% and 99.4% at the superfamily and class level according to the SCOP classification, and shows that on average 7.49 out of 10 proteins from the same superfamily are obtained among the top 10 matches. These results are competitive with the best previous methods.