Paper Info
Title
CTSS: a robust and efficient method for protein structure alignment based on local geometrical and biological features.
Abstract
We present a new method for conducting protein structure similarity searches, which improves on the accuracy, robustness, and 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. To improve matching accuracy, we smooth the noisy raw atomic coordinate data with spline fitting. To improve matching efficiency, we adopt a hierarchical coarse-to-fine strategy. We use an efficient hashing-based technique to 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 discover new, meaningful motifs that were not reported by other structure alignment methods.
Year
DOI
Venue
2003
10.1109/CSB.2003.1227316
CSB
Keywords
Field
DocType
matching accuracy,space curve matching,domain specific information,justgeometric information,structure alignment method,protein structure alignment,local geometrical,shape signature,biological features,awell-known local sequence alignment,wegenerate shape signature,efficient method,new method,matching efficiency,differential geometry,genetic algorithms,secondary structure,local alignment,visual inspection,protein structure,structure alignment,molecular biophysics,string matching,proteins,similarity search,sequence alignment
Spline (mathematics),String searching algorithm,Pairwise comparison,Structural alignment,Computer science,Robustness (computer science),Artificial intelligence,Invariant (mathematics),Hash function,Bioinformatics,Machine learning,Genetic algorithm
Conference
Volume
ISSN
ISBN
2
1555-3930
0-7695-2000-6
Citations 
PageRank 
References 
31
2.14
12
Authors
2
Name
Order
Citations
PageRank
Tolga Can126816.39
Yuan-Fang Wang2835137.72