Protein-protein interactions (PPIs) are highly important because of their main role in cellular processes and biochemical pathways; therefore, PPI can be very useful in the prediction of protein functions. Experimental techniques of PPI detection have certain drawbacks; hence computational methods can be used to complement wet lab techniques. Such methods can be applied to PPI prediction as well as validation of experimental results. Computational algorithms can lead to many false PPI predictions, which in turn result in non-adequate performance. We have developed a novel method based on combined analysis, entitled PPIccc. Three different descriptors for PPIccc included gene co-expression values, codon usage similarity and conservation of surface residues between protein products of a gene pair, which combined to predict PPI. Validation of results based on Human Protein Reference Database (HPRD) indicated improvement of performance in our proposed method. The results also revealed that conservation of surface residues between proteins in combination with codon usage similarity of their related genes increase the performance of PPI prediction. This means that codon usage similarity and surface residues between proteins (only sequence-based features) can predict PPIs as good as PPIccc.