QPhosphorylation　is　a　crucial　way　to　control　the　activity　of　proteins　in　many　eukaryotic　organisms　in　vivo.　Experimental　methods　to　determine　phosphorylation　sites　in　substrates　are　usually　restricted　by　the　in　vitro　condition　of　enzymes　and　very　intensive　in　time　and　labor.　Although　some　in　silico　methods　and　web　servers　have　been　introduced　for　automatic　detection　of　phosphorylation　sites,　sophisticated　methods　are　still　in　urgent　demand　to　further　improve　prediction　performances.　Protein　primary　sequences　can　help　predict　phosphorylation　sites　catalyzed　by　different　protein　kinase　and　most　computational　approaches　use　a　short　local　peptide　to　make　prediction.　However,　the　useful　information　may　be　lost　if　only　the　conservative　residues　that　are　not　close　to　the　phosphorylation　site　are　considered　in　prediction,　which　would　hamper　the　prediction　results.　A　novel　prediction　method　named　IEPP　(Information-Entropy　based　Phosphorylation　Prediction)　is　presented　in　this　paper　for　automatic　detection　of　potential　phosphorylation　sites.　In　prediction,　the　sites　around　the　phosphorylation　sites　are　selected　or　excluded　by　their　entropy　values.　The　algorithm　was　compared　with　other　methods　such　as　GSP　and　PPSP　on　the　ABL,　MAPK　and　PKA　PK　families.　The　superior　prediction　accuracies　were　obtained　in　various　measurements　such　as　sensitivity　(Sn)　and　specificity　(Sp).　Furthermore,　compared　with　some　online　prediction　web　servers　on　the　new　discovered　phosphorylation　sites,　IEPP　also　yielded　the　best　performance.　IEPP　is　another　useful　computational　resource　for　identification　of　PK-specific　phosphorylation　sites　and　it　also　has　the　advantages　of　simpleness,　efficiency　and　convenience.