A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins

We applied support vector machines to the prediction of the subcellular localization of transmembrane proteins, and compared the performance of different sequence kernels on this task. More specifically we measured prediction accuracy, computation time, number of kernel evaluations and number of support vectors for the spectrum, the full spectrum, the wildcard, the mismatch, the local-alignment and the residue-coupling kernel. The local-alignment achieved the highest prediction accuracy, with a Matthews correlation coefficient of 0.51, closely followed by the mismatch kernel. However, the local-alignment kernel was also the most time consuming kernel and seven times slower than the mismatch kernel. The spectrum kernel was the fastest kernel but linked to the highest number of support vectors and kernel evaluations. The residue-coupling kernel showed the lowest number of support vectors and kernel evaluations. No correlation between the number of support vectors and prediction accuracy could be observed. A localization predictor (TMPLoc) has been made available at http://pprowler.itee.uq.edu.au/TMPLoc