LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Gamma-carboxylation, one type of post-translational modifications, is involved in many human disease. However, very few computational methods for gamma-carboxylation site prediction are available. In this paper, we develop a novel method CarboxySVM which is based on support vector machine with radial basis function kernel to identify the gamma-carboxylation sites. In this method, we combine position specific scoring matrices (PSSM)-based evolutionary conservation scores and other sequences-derived descriptors. As a result, an accuracy of 91.2% is achieved on training dataset with fivefold cross validation, and 91.8% on the independent test dataset. It is demonstrated by empirical evaluation on benchmark datasets that our method outperforms several other modern predictors. Our model reveals that evolutionary conservation is higher in carboxylation sites, compared to non-carboxylation sites. The composition of arginine in carboxylation sites is higher than that of non-carboxylation sites. CarboxySVM can be downloaded from http://code.google.com/p/gamma-carboxylation/source/browse/trunk.