Erratum

The interactions between cytokines and their complementary receptors are the gateways to properly understand a large variety of cytokine-specific cellular activities such as immunological responses and cell differentiation. To discover novel cytokinereceptor interactions, an advanced support vector machines (SVMs) model, CytoSVM, was constructed in this study. This model was iteratively trained using 449 mammal (except rat) cytokine-receptor interactions and about 1 million virtually generated positive and negative vectors in an enriched way. Final independent evaluation by rat’s data received sensitivity of 97.4%, specificity of 99.2% and the Matthews correlation coefficient (MCC) of 0.89. This performance is better than normal SVM-based models. Upon this well-optimized model, a webbased server was created to accept primary protein sequence and present its probabilities to interact with one or several cytokines. Moreover, this model was applied to identify putative cytokine-receptor pairs in the whole genomes of human and mouse. Excluding currently known cytokine-receptor interactions, total 1609 novel cytokine-receptor pairs were discovered from human genome with probability 80% after further transmembrane analysis. These cover 220 novel receptors (excluding their isoforms) for 126 human cytokines. The screening results have been deposited in a database. Both the server and the database can be freely accessed at http://bioinf.xmu. edu.cn/software/cytosvm/ cytosvm.php. INTRODUCTION The binding of cytokines to their receptors on cell membranes triggers the cellular activities such as immunological regulation, cell growth, differentiation, apoptosis and migration in vertebrates (1). Therefore, characterization of novel cytokine-receptor pairs becomes the shortcut to understand these cytokine-mediated signal pathways. The traditional isolation and characterization methods for identification of cytokine-receptor pairs are significantly limited by their characteristics of short half life, low plasma concentrations, pleiotropy and redundancy. It has been improved by the applications of modern molecular technologies such as cloning technology. Furthermore, as a complementary solution to experimental approaches, searches for new members of cytokines or their receptors are now often conducted by identifying genes highly homologous to known cytokine/receptor genes. Currently, 203 human cytokine-receptor pairs have been characterized as presented in KEGG pathway database (2). Unfortunately, it has become more and more difficult to discover new partners of cytokine and receptor if no new sequence features were identified. Especially for those peptides without significant sequence similarity to known cytokines/receptors, their functions are difficult to be probed on the basis of homologous or clustering methods. Various alternative methods for describing protein interactions have been developed in recent years. These include evolutionary analysis (3,4), Hidden Markov Models (5), structural consideration (6–8), protein/gene fusion (9,10), motifs recognition (11), family classification by sequence clustering (12) and functional family prediction by statistical learning methods (13,14). Support vector machines (SVMs) is a two-class classifier, which has been previously used in the classification of cytokine families (http://www.bioinfo.tsinghua.edu.cn/%7Ehn/ CTKPred/index.html) (14). In this study, we constructed an improved SVMmodel, CytoSVM, for the identification of cytokine-receptor interactions on the basis of protein *To whom correspondence should be addressed. Tel: 86-0592-2182897; Fax: 86-0592-2181015; Email: appo@bioinf.xmu. edu.cn; zhiliang.ji@gmail.com; The authors wish it to be known that, in their opinion, the second and third authors contributed equally to this work