ProteinĘCprotein interactions (PPIs) are central to most biological
processes. Although efforts have been devoted to the development
of methodology for predicting PPIs and protein interaction
networks, the application of most existing methods is limited
because they need information about protein homology or the
interaction marks of the protein partners. In the present work, we
propose a method for PPI prediction using only the information of
protein sequences. This method was developed based on a learning
algorithm-support vector machine combined with a kernel
function and a conjoint triad feature for describing amino acids.
More than 16,000 diverse PPI pairs were used to construct the
universal model. The prediction ability of our approach is better
than that of other sequence-based PPI prediction methods because
it is able to predict PPI networks. Different types of PPI networks
have been effectively mapped with our method, suggesting that,
even with only sequence information, this method could be applied
to the exploration of networks for any newly discovered
protein with unknown biological relativity. In addition, such supplementary
experimental information can enhance the prediction
ability of the method.