Towards index-based similarity search for protein structure databases.
We propose two methods for finding similarities in protein structure databases. Our techniques extract feature vectors on triplets of SSEs (Secondary Structure Elements) of proteins. These feature vectors are then indexed using a multidimensional index structure. Our first technique considers the problem of finding proteins similar to a given query protein in a protein dataset. This technique quickly finds promising proteins using the index structure. These proteins are then aligned to the query protein using a popular pairwise alignment tool such as VAST. We also develop a novel statistical model to estimate the goodness of a match using the SSEs. Our second technique considers the problem of joining two protein datasets to find an all-to-all similarity. Experimental results show that our techniques improve the pruning time of VAST 3 to 3.5 times while keeping the sensitivity similar.
Camoğlu
Orhan
O
Department of Computer Science, University of California, Santa Barbara, 93106, USA. orhan@cs.ucsb.edu
Kahveci
Tamer
T
Singh
Ambuj K
AK
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003628:Database Management Systems; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins