Name
Abstract | ||
|---|---|---|
We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1142/S0219720004000533 | J. Bioinformatics and Computational Biology |
Keywords | Field | DocType |
hashing,similarity search | Small number,Structural alignment,Artificial intelligence,Nearest neighbor search,Pairwise comparison,Pattern recognition,Compact space,Invariant (mathematics),Hash function,Differential geometry,Bioinformatics,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
2 | 1 | 0219-7200 |
Citations | PageRank | References |
3 | 0.43 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tolga Can | 1 | 268 | 16.39 |
| Yuan-Fang Wang | 2 | 835 | 137.72 |