Protein tertiary structure defines its functions, classification and binding sites. Similar structural characteristics between two proteins often lead to the similar characteristics thereof. Determining structural similarity accurately in real time is a crucial research issue. In this paper, we present a novel and effective scoring scheme that is dependent on novel features extracted from protein alpha carbon distance matrices. Our scoring scheme is inspired from pattern recognition and computer vision. Our method is significantly better than the current state of the art methods in terms of family match of pairs of protein structures and other statistical measurements. The effectiveness of our method is tested on standard benchmark structures. A web service is available at this http URL where you can get the similarity measurement score between two protein structures based on our method.
@cite_16: 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. Our system is able to return search results in ranked order from a database with 46075 chains in seconds, exhibiting a reasonably high degree of precision. To our knowledge, this is the first real-time search engine for protein structure comparison. ProteinDBS provides two types of query method: query by Protein Data Bank protein chain ID and by new structures uploaded by users. The system is hosted at http: ProteinDBS.rnet.missouri.edu.
ProteinDBS @cite_16 is another method that uses some common features of CBIR (Content Based Image Retrieval) to compare @math -carbon distance matrix images. This method is much faster than the previous ones as it compares only some specific image features. Notably however, the feature preprocessing done by ProteinDBS is computationally expensive.