Can simple codon pair usage predict protein-protein interaction?
Can simple codon pair usage predict protein-protein interaction?
复制标题
简单的密码子对使用可以预测蛋白质-蛋白质相互作用吗？
DOI：
10.1039/c2mb05427b
复制
发表时间：
2012-01-01
影响因子：
--
通讯作者：
Zhang, Ziding
中科院分区：
文献类型：
--
作者：
Zhou, Yuan;Zhou, Ying-Si;Zhang, Ziding
破译蛋白质之间的功能相互作用是生物学中的巨大挑战之一。基于序列的无同源编码方案已越来越多地应用于通过统计或机器学习方法开发有前途的蛋白质-蛋白质相互作用（PPI）预测器。本文分析了酵母菌密码子对使用与PPIs之间的关系。我们发现相互作用蛋白对的密码子对使用与随机预期有显著差异。这激发了一种预测ppi的新方法的发展，将密码子对频率差作为支持向量机预测器的输入，称为CCPPI。基于酵母PPI数据集的10倍交叉验证测试表明，CCPPI比其他基于序列的编码方案表现更好。此外，当在不平衡的大规模数据集上进行测试时，它的排名最好。尽管CCPPI与许多PPI预测因子一样存在高假阳性率，但对预测真阳性的统计分析证实，CCPPI的成功部分归因于其捕获相互作用蛋白对之间的蛋白质组共表达和功能相似性的能力。我们的研究结果表明，相互作用的蛋白质对的密码子以协调的方式进化，因此它们提供了基于氨基酸的编码方案之外的额外信息。CCPPI已在http://protein.cau.edu.cn/ccppi上免费提供。
Deciphering functional interactions between proteins is one of the great challenges in biology. Sequence-based homology-free encoding schemes have been increasingly applied to develop promising protein-protein interaction (PPI) predictors by means of statistical or machine learning methods. Here we analyze the relationship between codon pair usage and PPIs in yeast. We show that codon pair usage of interacting protein pairs differs significantly from randomly expected. This motivates the development of a novel approach for predicting PPIs, with codon pair frequency difference as input to a Support Vector Machine predictor, termed as CCPPI. 10-fold cross-validation tests based on yeast PPI datasets with balanced positive-to-negative ratios indicate that CCPPI performs better than other sequence-based encoding schemes. Moreover, it ranks the best when tested on an unbalanced large-scale dataset. Although CCPPI is subjected to high false positive rates like many PPI predictors, statistical analyses of the predicted true positives confirm that the success of CCPPI is partly ascribed to its capability to capture proteomic co-expression and functional similarities between interacting protein pairs. Our findings suggest that codon pairs of interacting protein pairs evolve in a coordinated manner and consequently they provide additional information beyond amino acids-based encoding schemes. CCPPI has been made freely available at: http://protein.cau.edu.cn/ccppi.