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An important limitation of current protein secondary structure prediction tools is the bad performance in locating the secondary structure boundaries. Efficiently utilize the residue position-specific preference around secondary structure boundaries can help to resolve this problem. TLSSP (two level secondary structure predictor), proposed in this study, used a two-level strategy to utilize these properties efficiently and find the optimal global secondary structure. In TLSSP a set of binary classifiers were designed to recognize the boundaries of helices and strands firstly, then a global model based on condition random fields (CRFs) was built to predict the secondary structures. Five-fold cross-validation test on EVA dataset (containing 3744 proteins provided by EVA service) indicated that, TLSSP can get quite good performance on both boundaries prediction and global secondary structure prediction.