Protein–protein　interaction　plays　a　fundamental　role　in　many　biological　processes　and　diseases.　Characterizing　protein　interaction　sites　is　crucial　for　the　understanding　of　the　mechanism　of　protein–protein　interaction　and　their　cellular　functions.　In　this　paper,　we　proposed　a　method　based　on　integrated　support　vector　machine　(SVM)　with　a　hybrid　kernel　to　predict　protein　interaction　sites.　First,　a　number　of　features　of　the　protein　interaction　sites　were　extracted.　Secondly,　the　technique　of　sliding　window　was　used　to　construct　a　protein　feature　space　based　on　the　influence　of　the　adjacent　residues.　Thirdly,　to　avoid　the　impact　of　imbalance　of　the　data　set　on　prediction　accuracy,　we　employed　boost-strap　to　re-sample　the　data.　Finally,　we　built　a　SVM　classifier,　whose　hybrid　kernel　comprised　a　Gaussian　kernel　and　a　Polynomial　kernel.　In　addition,　an　improved　particle　swarm　optimization　(PSO)　algorithm　was　applied　to　optimize　the　SVM　parameters.　Experimental　results　show　that　the　PSO-optimized　SVM　classifier　outperforms　existing　methods.　©　2016,　Springer-Verlag　Berlin　Heidelberg.