In recent years, homology-based and signal-based methods have been proposed for predicting the subcellular localization of proteins. While it has been known that homology-based methods can detect more subcellular locations than signal-based methods, the former generally requires a lot more computational resources during both training and prediction. The problem will become intractable for annotating large databases. One possible solution is to reduce the sequence length. This paper proposes to use the cleavage sites detected by signal-based methods (e.g., TargetP) to extract the sequence or profile segments that contain the most localization information for alignment. It was found that the method can reduce computation time of full-length alignment by 27-fold at a cost of only 8% reduction in prediction accuracy. Moreover, the method can increase the accuracy by 0.8% and at the same time reduce the computation time by 41%. Results also show that cutting the sequences at the cleavage sites detected by TargetP is better than cutting them at a fixed position.