About PSL101 is a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machine (SVM) model and a structure homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. These features include amino acid composition, di-peptide composition, relative solvent accessibility, secondary structure, signal peptides, transmembrane α-helices, transmembrane β-barrels, twin arginine translocase motifs, and non-classical protein secretion. In the structure homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% on the standard benchmark data sets.
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