iLoops: a protein-protein interaction prediction server based on structural features

SUMMARY Protein-protein interactions play a critical role in many biological processes. Despite that, the number of servers that provide an easy and comprehensive method to predict them is still limited. Here, we present iLoops, a web server that predicts whether a pair of proteins can interact using local structural features. The inputs of the server are as follows: (i) the sequences of the query proteins and (ii) the pairs to be tested. Structural features are assigned to the query proteins by sequence similarity. Pairs of structural features (formed by loops or domains) are classified according to their likelihood to favor or disfavor a protein-protein interaction, depending on their observation in known interacting and non-interacting pairs. The server evaluates the putative interaction using a random forest classifier. AVAILABILITY iLoops is available at http://sbi.imim.es/iLoops.php CONTACT baldo.oliva@upf.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  Elisenda Feliu,et al.  Understanding protein-protein interactions using local structural features. , 2013, Journal of molecular biology.

[3]  Tim J. P. Hubbard,et al.  Data growth and its impact on the SCOP database: new developments , 2007, Nucleic Acids Res..

[4]  Baldomero Oliva,et al.  ArchDB: automated protein loop classification as a tool for structural genomics , 2004, Nucleic Acids Res..

[5]  Charles DeLisi,et al.  Predictome: a database of putative functional links between proteins , 2002, Nucleic Acids Res..

[6]  A. Barabasi,et al.  High-Quality Binary Protein Interaction Map of the Yeast Interactome Network , 2008, Science.

[7]  Alex W. Wilkinson,et al.  Computational prediction of protein-protein interactions , 2012 .

[8]  Leonardo G. Trabuco,et al.  Negative protein-protein interaction datasets derived from large-scale two-hybrid experiments. , 2012, Methods.

[9]  B. Rost Twilight zone of protein sequence alignments. , 1999, Protein engineering.

[10]  Julie M. Sahalie,et al.  An experimentally derived confidence score for binary protein-protein interactions , 2008, Nature Methods.

[11]  Damian Szklarczyk,et al.  The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored , 2010, Nucleic Acids Res..

[12]  Albert Chan,et al.  PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs , 2006, BMC Bioinformatics.

[13]  M. Gerstein,et al.  A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data , 2003, Science.

[14]  Baldomero Oliva,et al.  Biana: a software framework for compiling biological interactions and analyzing networks , 2010, BMC Bioinformatics.

[15]  Robert B. Russell,et al.  InterPreTS: protein Interaction Prediction through Tertiary Structure , 2003, Bioinform..

[16]  Dmitrij Frishman,et al.  The Negatome database: a reference set of non-interacting protein pairs , 2009, Nucleic Acids Res..

[17]  William Stafford Noble,et al.  Choosing negative examples for the prediction of protein-protein interactions , 2006, BMC Bioinformatics.

[18]  Bonnie Berger,et al.  Struct2Net: a web service to predict protein–protein interactions using a structure-based approach , 2010, Nucleic Acids Res..