Prediction of protein subcellular localization based on variable-length motifs detection and dissimilarity based classification

Predict the function of unknown proteins is one of the principal goals in computational biology. The subcellular localization of a protein allows further understanding its structure and molecular function. Numerous prediction techniques have been developed, usually focusing on global information of the protein. But, predictions can be done through the identification of functional sub-sequence patterns known as motifs. For motifs discovery problem, many methods requires a predefined fixed window size in advance and aligned sequences. To confront these problems we proposed a method based on variable length motifs characterization and detection using the continuous wavelet transform (CWT) and a dissimilarity space representation. For analyzing the motifs results generated by our approach, we divide the entire dataset into training (60%) and validation (40%). A Support Vector Machine (SVM) classifier is used as predictor for validation set. The highest Sn = 82.58% and Sp = 92.86%, across 10-fold cross validation, is obtained for endosome proteins. Average results Sn = 74% and Sp = 75.58% are comparable to current state of the art. For data sets whose identity is low (< 40%), the motifs characterization and localization based on CWT shows a good performance and the interpretability of the subsequences in each subcellular localization.