Interaction prediction and classification of PDZ domains

BMC Bioinformatics

Research Article

Attila Gursoy¹  Ozlem Keskin¹  Sibel Kalyoncu¹ 

[1] Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, Rumelifeneri Yolu, 34450, Sariyer, Istanbul, Turkey;

关键词: Cystic Fibrosis Transmembrane Conductance Regulator;    Area Under Curve;    Fluorescence Polarization;    Interaction Prediction;    Feature Vector Space;   

DOI  :  10.1186/1471-2105-11-357

 received in 2010-04-14, accepted in 2010-06-30,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundPDZ domain is a well-conserved, structural protein domain found in hundreds of signaling proteins that are otherwise unrelated. PDZ domains can bind to the C-terminal peptides of different proteins and act as glue, clustering different protein complexes together, targeting specific proteins and routing these proteins in signaling pathways. These domains are classified into classes I, II and III, depending on their binding partners and the nature of bonds formed. Binding specificities of PDZ domains are very crucial in order to understand the complexity of signaling pathways. It is still an open question how these domains recognize and bind their partners.ResultsThe focus of the current study is two folds: 1) predicting to which peptides a PDZ domain will bind and 2) classification of PDZ domains, as Class I, II or I-II, given the primary sequences of the PDZ domains. Trigram and bigram amino acid frequencies are used as features in machine learning methods. Using 85 PDZ domains and 181 peptides, our model reaches high prediction accuracy (91.4%) for binary interaction prediction which outperforms previously investigated similar methods. Also, we can predict classes of PDZ domains with an accuracy of 90.7%. We propose three critical amino acid sequence motifs that could have important roles on specificity pattern of PDZ domains.ConclusionsOur model on PDZ interaction dataset shows that our approach produces encouraging results. The method can be further used as a virtual screening technique to reduce the search space for putative candidate target proteins and drug-like molecules of PDZ domains.

【 授权许可】

Unknown   
© Kalyoncu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.