Xu Jin-Rui, Zhang Jing-Xian, Han Bu-Cong, Liang Liang, Ji Zhi-Liang
Key Laboratory for Cell Biology & Tumor Cell Engineering, the Ministry of Education of China, School of Life Sciences, Xiamen University, Xiamen 361005, FuJian Province, PR China.
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W538-42. doi: 10.1093/nar/gkm254. Epub 2007 May 25.
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 cytokine-receptor 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 web-based 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 approximately 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.
细胞因子与其互补受体之间的相互作用是正确理解多种细胞因子特异性细胞活动（如免疫反应和细胞分化）的关键。为了发现新的细胞因子 - 受体相互作用，本研究构建了一种先进的支持向量机（SVM）模型，即细胞因子支持向量机（CytoSVM）。该模型使用449种哺乳动物（大鼠除外）的细胞因子 - 受体相互作用以及约100万个虚拟生成的正负向量以富集的方式进行迭代训练。通过大鼠数据进行的最终独立评估得到的灵敏度为97.4%，特异性为99.2%，马修斯相关系数（MCC）为0.89。该性能优于基于普通支持向量机的模型。基于这个优化良好的模型，创建了一个基于网络的服务器，用于接受原始蛋白质序列并呈现其与一种或几种细胞因子相互作用的概率。此外，该模型还被应用于在人类和小鼠的全基因组中识别推定的细胞因子 - 受体对。在排除当前已知的细胞因子 - 受体相互作用后，经过进一步的跨膜分析，从人类基因组中总共发现了1609对新的细胞因子 - 受体对，概率约为80%。这些包括针对126种人类细胞因子的220种新受体（不包括其异构体）。筛选结果已存入数据库。服务器和数据库均可通过http://bioinf.xmu.edu.cn/software/cytosvm/cytosvm.php免费访问。
