Prediction of subcellular localizations using amino acid composition and order.

Subcellular localization is important for proteins to function. For the prediction of subcellular localizations, we have developed a method, SortPred, using the amino acid composition and order. The composition represents the global features, e.g., the amino acid composition in the full or partial sequences, while the order represents the local features, e.g., the amino acid sequence order. The former was represented by neural networks and the latter was represented by a hidden Markov model. This method predicted the signal peptides (SP), the mitochondrial targeting peptides (mTP), the chloroplast transit peptides (cTP), and the nuclear or cytosolic sequences (other) comparing together the previous methods, this method achieved slightly higher prediction accuracy, 86% for plant and 91% for non-plant. We analyzed the trained neural networks and hidden Markov models and found out that these models well represent the biological features of the sequences.