TooT-SC: Predicting Eleven Substrate Classes of Transmembrane Transport Proteins

Abstract

BackgroundTransporters form a significant proportion of the proteome and play an important role in mediating the movement of compounds across membranes. Transport proteins are difficult to characterize experimentally, so there is a need for computational tools that predict the substrates transported in order to annotate the large number of genomes being sequenced. Recently we developed a dataset of eleven substrate classes from Swiss-Prot using the ChEBI ontology as the basis for the definition of the classes. ResultsWe extend our earlier work TranCEP, which predicted seven substrate classes, to the new dataset with eleven substrate classes. Like TranCEP, TooT-SC combines pairwise amino acid composition (PAAC) of the protein, with evolutionary information captured in a multiple sequence alignment (MSA) using TM-Coffee, and restriction to important positions of the alignment using TCS. Our experimental results show that TooT-SC significantly outperforms the state-of-the-art predictors, including our earlier work, with an overall MCC of 0.82 and the MCC for the eleven classes ranging from 0.66 to 1.00. ConclusionTooT-SC is a useful tool with high performance covering a broad range of substrate classes. The results quantify the contribution made by each type of information used during the prediction process. We believe the methodology is applicable more generally for protein sequence analysis.