Because　of　the　importance　of　phosphorylation　and　the　mystery　of　the　corresponding　mechanism　in　vivo,　predicting　potential　phosphorylation　sites　with　protein　kinases　(PKs)　information　is　a　critical　and　intriguing　challenge　in　bioinformatics.　Although　some　in　silico　methods　have　been　introduced　for　this　purpose,　sophisticated　methods　are　still　in　urgent　demand　to　further　improve　prediction　performances.　In　this　paper,　we　present　a　novel,　versatile　and　comprehensive　program,　PSPhos　(Profile　SVM　for　Phosphorylation　site　prediction),　based　on　profile　Support　Vector　Machine.　We　estimated　the　performance　of　PSPhos　using　experimentally　verified　substrate　sites　of　two　important　PK　families:　ABL　and　DNA-PK,　and　compared　the　performance　with　the　GPS　and　PPSP　algorithms.　With　the　Jack-knife　tests,　PSPhos　consistently　performed　similar　to　20%　better　in　sensitivity.　Then　a　more　comprehensive　comparison　between　PSPhos　and　most　available　web　servers　for　phosphorylation　prediction　was　made　on　two　manually-curated　testing　data　of　novel　substrate　sites.　With　a　high　specificity　of　100%,　the　sensitivity　obtained　by　PSPhos　on　the　ABL　data　set　was　16%　higher　than　the　best　result　obtained　by　several　existing　methods.　On　DNA-PK　data,　only　PSPhos　could　successfully　identify　true　phosphorylation　sites　with　high　stringency　for　minimal　false　positives.　All　these　results　indicate　that　PSPhos　is　a　useful　computational　resource　for　the　identification　of　PK-specific　phosphorylation　sites,　and　can　provide　helpful　insights　for　future　experimental　design　and　verification.