ProteinDBS v2.0: a web server for global and local protein structure search.

ProteinDBS v2.0 is a web server designed for efficient and accurate comparisons and searches of structurally similar proteins from a large-scale database. It provides two comparison methods, global-to-global and local-to-local, to facilitate the searches of protein structures or substructures.

Efficient SCOP-fold classification and retrieval using index-based protein substructure alignments.

Motivation: To investigate structure-function relationships, life sciences researchers usually retrieve and classify proteins with similar substructures into the same fold. A manually constructed database, SCOP, is believed to be highly accurate; however, it is labor intensive. Another known method, DALI, is also precise but computationally expensive.

A fast SCOP fold classification system using content-based E-Predict algorithm.

Background: Domain experts manually construct the Structural Classification of Protein (SCOP) database to categorize and compare protein structures. Even though using the SCOP database is believed to be more reliable than classification results from other methods, it is labor intensive. To mimic human classification processes, we develop an automatic SCOP fold classification system to assign possible known SCOP folds and recognize novel folds for newly-discovered proteins.

ProteinDBS: a real-time retrieval system for protein structure comparison.

We have developed a web server (ProteinDBS) for the life science community to search for similar protein tertiary structures in real time. This system applies computer visualization techniques to extract the predominant visual patterns encoded in two-dimensional distance matrices generated from the three-dimensional coordinates of protein chains. When meaningful contents, represented in a multi-dimensional feature space, have been extracted from distance matrices, an advanced indexing structure, Entropy Balanced Statistical (EBS) k-d tree, is utilized to index the data.