The web-server predSucc-Site used to predict the lysine succinylation in protein. The Lysine succinylation is found as an important post-translational modification where succinyle group is added to a Lys (K) residue of a protein molecule. It plays major role not only in regulating the cellular processes but also associated with some diseases. As a result, it requires an easiest way to detect succinylation modification in proteins. However, since the experimental technologies are costly and time-consuming, so itís quite hard to detect the succinylation modification timely at lost to face the explosive growth of protein sequences in postgenomic age. In this context, an accurate computational method for predicting succinylation sites is an urgent issue which can be useful for drug development. In this study, a novel computational tool termed predSucc-Site has been developed to predict protein succinylation sites by (1) incorporating the sequence-coupled information in to the general pseudo amino acid composition, (2) balancing the effect of skewed training dataset by Different Error Costs (DEC) method, and (3) constructing a predictor using support vector machine as classifier. The experimental results show that the predSucc-Site predictor achieves an average AUC score of 0.97 found in the 5 times complete run of 5-fold cross-validation set.