預測蛋白質之間的交互作用是一個重要而且研究相當完整的議題。大多數生物作用產生必須經歷蛋白質交互作用，異常的交互作用可能與某些神經系統症候群有關，因此，指出蛋白質之間是否有關連是必要的。 發生交互作用的蛋白質組，應該落在細胞中相同的位置。目前已存在的方法，大多建立於以蛋白質序列或是特定片段的信號來預測交互作用，很少將蛋白質的位置列入特性。而我們建立一個整合系統，能夠以蛋白質落在真核細胞中的位置為基礎，預測是否產生交互作用。我們取用蛋白質序列的組成、蛋白質的區塊、在細胞中的位置來建構這個系統。我們建立不同的模組來預測交互作用，依照輸入蛋白質組的位置選取其所屬位置的模組。我們的方法提高了蛋白質交互作用的預測效能，若有更完整的蛋白質交互作用以及位置資訊，將得到更高準度的預測。Protein–protein interactions are importance for almost every process in living cell. Abnormal interactions may have implications in a number of neurological syndromes. Therefore, it is crucial to recognize the association and dissociation of protein molecules. Current available computational methods of prediction of protein–protein interaction extract information from amino acid sequence or signal peptide. There are few method consider subcellular localization information. The method presented in this paper is based on the assumption that two proteins should appear on same subcellular localization to perform interaction. We develop an integrated system which based on a learning algorithm-support vector machine to predict protein–protein interactions. We construct training models for different subcellular localization. Each test protein pair request one training model to predict according to its localization. This method is take protein sequence composition, protein domains and subcellular localization information as features. The prediction ability of our method is better than other sequence-based protein–protein interaction prediction methods. In addition, a more complete data of protein-protein interactions and subcellular localizations can enhance the prediction ability of the method.
