PSIST: indexing protein structures using suffix trees

Abstract

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 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.

DDPIn - Distance and density based protein indexing

“…As stated earlier, PSI [3] and PSIST [6] employ trees for storing their vectors. PSI uses SSEs as fundamental objects.…”

“…Techniques in this group can be further divided into two categories depending on the type of indexing methodology they use. In one category reside algorithms using indexing trees to speed-up the querying such as PSI [3] and PSIST [6] and in the other algorithms using geometric hashing [21] -ProGreSS [1] or CTSS [2]. Since our paper introduces a method falling into the group of algorithms exploiting feature vectors, we describe these algorithms a bit more thoroughly in the following section.…”

Searching Protein 3-D Structures for Optimal Structure Alignment Using Intelligent Algorithms and Data Structures

“…The area of protein 3-D structure encoding has been widely studied by authors in previous works, e.g., [6], [26], [27]. Since the protein backbone is the sequence of the amino acid residues (in 3-D space), we are able to encode this backbone into the sequence of integers in the following manner.…”

“…Several studies were developed for indexing protein tertiary structure [6], [7]. These studies are targeted mainly at some kind of selection of the PDB database.…”

Prosima: Protein similarity algorithm

“…Several studies were developed for indexing protein tertiary structure [5,20]. These studies are targeted mainly at some kind of selection of the PDB database.…”