Protein　carbonylation　is　one　of　the　most　pervasive　oxidative　stress-induced　post-translational　modifications　(PTMs),　which　plays　a　significant　role　in　the　etiology　and　progression　of　several　human　diseases.　It　has　been　regarded　as　a　biomarker　of　oxidative　stress　due　to　its　relatively　early　formation　and　stability　compared　with　other　oxidative　PTMs.　Only　a　subset　of　proteins　is　prone　to　carbonylation　and　most　carbonyl　groups　are　formed　from　lysine　(K),　arginine　(R),　threonine　(T)　and　proline　(P)　residues.　Recent　advancements　in　analysis　of　the　PTM　by　mass　spectrometry　provided　new　insights　into　the　mechanisms　of　protein　carbonylation,　such　as　protein　susceptibility　and　exact　modification　sites.　However,　the　experimental　approaches　to　identifying　carbonylation　sites　are　costly,　time-consuming　and　capable　of　processing　a　limited　number　of　proteins,　and　there　is　no　bioinformatics　method　or　tool　devoted　to　predicting　carbonylation　sites　of　human　proteins　so　far.　In　the　paper,　a　computational　method　is　proposed　to　identify　carbonylation　sites　of　human　proteins.　The　method　extracted　four　kinds　of　features　and　combined　the　minimum　Redundancy　Maximum　Relevance　(mRMR)　feature　selection　criterion　with　weighted　support　vector　machine　(WSVM)　to　achieve　total　accuracies　of　85.72%,　85.95%,　83.92%　and　85.72%　for　K,　R,　T　and　P　carbonylation　site　predictions　respectively　using　10-fold　cross-validation.　The　final　optimal　feature　sets　were　analysed,　the　position-specific　composition　and　hydrophobicity　environment　of　flanking　residues　of　modification　sites　were　discussed.　In　addition,　a　software　tool　named　CarSPred　has　been　developed　to　facilitate　the　application　of　the　method.　Datasets　and　the　software　involved　in　the　paper　are　available　at　https://sourceforge.net/projects/hqlstudio/files/CarSPred-1.0/.