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Issue 5, 2012
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Can simple codon pair usage predict proteinprotein interaction?

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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 proteinprotein 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.

Graphical abstract: Can simple codon pair usage predict protein–protein interaction?

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Publication details

The article was received on 17 Oct 2011, accepted on 12 Feb 2012 and first published on 05 Mar 2012

Article type: Method
DOI: 10.1039/C2MB05427B
Citation: Mol. BioSyst., 2012,8, 1396-1404
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    Can simple codon pair usage predict proteinprotein interaction?

    Y. Zhou, Y. Zhou, F. He, J. Song and Z. Zhang, Mol. BioSyst., 2012, 8, 1396
    DOI: 10.1039/C2MB05427B

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