PSI: indexing protein structures for fast similarity search

We consider the problem of finding similarities in protein structure databases. Our techniques extract feature vectors on triplets of SSEs (Secondary Structure Elements). Later, these feature vectors are indexed using a multidimensional index structure. Our first technique finds proteins similar to a query protein in a protein dataset. This technique quickly prunes unpromising proteins using the index structure. The remaining proteins are then aligned using a popular 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.

• Type: Conference
• Year: 2003
• Where: ISMB
• Authors: Orhan Çamoglu, Tamer Kahveci, Ambuj K. Singh
• Keywords: Computational Biology, Feature Vectors, ISMB 2003, Protein, Protein Datasets