A novel method to compare protein structures using local descriptors.

TitleA novel method to compare protein structures using local descriptors.
Publication TypeJournal Article
Year of Publication2011
AuthorsDaniluk, Paweł, and Lesyng Bogdan
JournalBMC bioinformatics
Date Published08/2011
KeywordsAlgorithms, Animals, Bacterial Proteins, Carrier Proteins, Computational Biology, GTP Phosphohydrolases, Humans, Models, Molecular, Proteins, Saposins, Structural Homology, Protein
AbstractBACKGROUND: Protein structure comparison is one of the most widely performed tasks in bioinformatics. However, currently used methods have problems with the so-called "difficult similarities", including considerable shifts and distortions of structure, sequential swaps and circular permutations. There is a demand for efficient and automated systems capable of overcoming these difficulties, which may lead to the discovery of previously unknown structural relationships. RESULTS: We present a novel method for protein structure comparison based on the formalism of local descriptors of protein structure - DEscriptor Defined Alignment (DEDAL). Local similarities identified by pairs of similar descriptors are extended into global structural alignments. We demonstrate the method's capability by aligning structures in difficult benchmark sets: curated alignments in the SISYPHUS database, as well as SISY and RIPC sets, including non-sequential and non-rigid-body alignments. On the most difficult RIPC set of sequence alignment pairs the method achieves an accuracy of 77% (the second best method tested achieves 60% accuracy). CONCLUSIONS: DEDAL is fast enough to be used in whole proteome applications, and by lowering the threshold of detectable structure similarity it may shed additional light on molecular evolution processes. It is well suited to improving automatic classification of structure domains, helping analyze protein fold space, or to improving protein classification schemes. DEDAL is available online at http://bioexploratorium.pl/EP/DEDAL.
Alternate JournalBMC Bioinformatics
PubMed ID21849047