pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties

Background: Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively. Results: In this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the datase...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Deepak Sarda, Gek Huey Chua, Kuo-Bin Li, Arun Krishnan
• Keywords: Amino Acid, BMCBI 2005, Protein Subcellular Localization, Proteins